We are tasked to present to the Chief Marketing Officer our analysis of data set to increase total purchases and be more efficient with spending on the marketing department. If we are able to successfully correlate and come up with any significant regression models to show what variables are the most impactful in spending, we would consider our data research a success.
We are given the data of 30 variables in our data to analyze. We are given data of customers such as: Year of birth, educational level, country, spending habits on certain items, online shopping, and other characteristics on a customer. From there, we will decide to further analyze and what to take out of our model in terms of importance.
# Import libraries
library(readxl)
library(BBmisc)
library(fastDummies)
library(dplyr)
library(ggplot2)
# Import data
mark_cmp <- read_excel("datasets_marketing_campaign_SF.xlsx")
# Inspect
colSums(is.na(mark_cmp))## ID Year_Birth Education Marital_Status
## 0 0 0 0
## Income Kidhome Teenhome Dt_Customer
## 24 0 0 0
## Recency MntWines MntFruits MntMeatProducts
## 0 0 0 0
## MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases
## 0 0 0 0
## NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth
## 0 0 0 0
## AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1
## 0 0 0 0
## AcceptedCmp2 Complain Z_CostContact Z_Revenue
## 0 0 0 0
## Response Country
## 0 0# Convert
mark_cmp <- as.data.frame(mark_cmp)library(tidyr)
# Remove NAs
mark_cmp <- drop_na(mark_cmp)
# Change date variable from character to date
mark_cmp$Dt_Customer <- as.Date(mark_cmp$Dt_Customer)
# Change Year Birth to Age
mark_cmp$actual_age <- 2021 - mark_cmp$Year_Birth
# Create sub dfs for types numerical, character and dates
bool <- sapply(mark_cmp,is.numeric)
num_cols <- mark_cmp[,bool]
#bool <- sapply(mark_cmp,is.Date)
#date_cols <- mark_cmp[,bool]
bool <- sapply(mark_cmp,is.character)
cat_cols <- mark_cmp[,bool]
# Create dummy variables for the categories
cat_dummy <- dummy_cols(cat_cols, select_columns = c('Education', 'Marital_Status','Country'))
# Remove the character entries
cat_dummy <- cat_dummy[,4:ncol(cat_dummy)]
# Combine data
cat_num_cols <- cbind(cat_dummy,num_cols)
# Write csv
write.csv(mark_cmp,"C:/Users/LK/Nextcloud7/Personal/Docs/case-studies/Marketing Campaign/data.csv", row.names = FALSE)# Plot Histogram of customer age
hist(mark_cmp$actual_age,
main="Histogram of Actual Age",
xlab="Age of customers",
border="black",
col="blue",
xlim=c(10, 90),
las=1,
breaks=20)
Label: Web Purchases Features: All numerical
Use of linear regression to classify the important variables that help us predict web purchases.
# Scale the data
scaled_mark = normalize(mark_cmp, method = "range", range = c(0, 1))
# Split into training and test
train_index <- sample(1:nrow(mark_cmp),size=0.8*nrow(mark_cmp))
mark_train <- mark_cmp[train_index,]
mark_test <- mark_cmp[-train_index,]
# Train the model
model <- lm(NumWebPurchases ~ ., data=mark_train)
summary(model)##
## Call:
## lm(formula = NumWebPurchases ~ ., data = mark_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.0304 -0.9534 -0.0906 0.8789 22.7327
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.684e+00 1.026e+01 -0.262 0.7936
## ID 3.522e-06 1.460e-05 0.241 0.8095
## Year_Birth -4.914e-03 4.523e-03 -1.086 0.2775
## EducationBasic -2.833e-01 3.381e-01 -0.838 0.4021
## EducationGraduation 1.947e-02 1.718e-01 0.113 0.9098
## EducationMaster -5.907e-02 2.019e-01 -0.293 0.7698
## EducationPhD 1.810e-01 1.996e-01 0.907 0.3647
## Marital_StatusAlone 3.422e+00 2.007e+00 1.705 0.0885 .
## Marital_StatusDivorced 1.503e+00 1.439e+00 1.045 0.2962
## Marital_StatusMarried 1.613e+00 1.432e+00 1.126 0.2602
## Marital_StatusSingle 1.557e+00 1.434e+00 1.085 0.2780
## Marital_StatusTogether 1.589e+00 1.433e+00 1.108 0.2679
## Marital_StatusWidow 1.573e+00 1.453e+00 1.082 0.2792
## Marital_StatusYOLO 2.480e+00 2.010e+00 1.234 0.2174
## Income 3.228e-05 4.393e-06 7.347 3.10e-13 ***
## Kidhome -8.052e-01 1.200e-01 -6.712 2.59e-11 ***
## Teenhome 1.419e-01 1.128e-01 1.258 0.2086
## Dt_Customer 5.719e-04 2.732e-04 2.093 0.0365 *
## Recency -2.037e-04 1.694e-03 -0.120 0.9043
## MntWines 2.123e-03 2.622e-04 8.094 1.08e-15 ***
## MntFruits 1.019e-03 1.672e-03 0.609 0.5423
## MntMeatProducts -8.667e-04 3.769e-04 -2.299 0.0216 *
## MntFishProducts 8.706e-04 1.296e-03 0.672 0.5018
## MntSweetProducts 7.728e-03 1.634e-03 4.729 2.43e-06 ***
## MntGoldProds 9.768e-03 1.132e-03 8.627 < 2e-16 ***
## NumDealsPurchases 2.122e-01 3.156e-02 6.724 2.39e-11 ***
## NumCatalogPurchases -2.324e-02 2.865e-02 -0.811 0.4175
## NumStorePurchases 1.271e-01 2.292e-02 5.545 3.40e-08 ***
## NumWebVisitsMonth 3.685e-01 3.251e-02 11.336 < 2e-16 ***
## AcceptedCmp3 1.227e-01 1.951e-01 0.629 0.5294
## AcceptedCmp4 7.614e-02 2.237e-01 0.340 0.7337
## AcceptedCmp5 -5.279e-01 2.359e-01 -2.237 0.0254 *
## AcceptedCmp1 -9.955e-02 2.273e-01 -0.438 0.6615
## AcceptedCmp2 -1.853e+00 4.278e-01 -4.332 1.57e-05 ***
## Complain 5.923e-01 4.858e-01 1.219 0.2229
## Z_CostContact NA NA NA NA
## Z_Revenue NA NA NA NA
## Response 2.775e-01 1.641e-01 1.691 0.0911 .
## CountryCA 9.053e-02 2.268e-01 0.399 0.6898
## CountryGER -4.932e-02 2.748e-01 -0.180 0.8576
## CountryIND 8.051e-02 2.648e-01 0.304 0.7611
## CountryME -5.631e-01 1.422e+00 -0.396 0.6922
## CountrySA 2.223e-02 2.197e-01 0.101 0.9194
## CountrySP -3.258e-02 1.946e-01 -0.167 0.8671
## CountryUS 8.944e-02 2.794e-01 0.320 0.7490
## actual_age NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.978 on 1729 degrees of freedom
## Multiple R-squared: 0.5032, Adjusted R-squared: 0.4911
## F-statistic: 41.69 on 42 and 1729 DF, p-value: < 2.2e-16# Let the model predict values for the test data
pred <- predict(model,mark_test)
# Get residuals
res <- resid(model)
# Produce residual vs. fitted plot
plot(fitted(model), res, main="Residual vs. fitted Values", xlab='Fitted', ylab='Residuals')
# Produce predicted vs actual values
plot(pred,mark_test$NumWebPurchases, main="Predicted vs. actual Values", xlab='Predicted Values', ylab='Actual Values')
The first regression we run was built including all the variables we had from the case. This regression was meant to give us a first screening of the statistical significance every single variable brought into the model. Looking at the p-value from each row of the summary (which we decided to be < .05 to have a significant impact,) we could identify the variable with the highest potential. If we could have expected some variables to have an impact on web purchases, it would be number of web visits per month or number of store purchases. While on the other we see variables, initially we seemed not related to our goal, having a very high statistical significance in the model (i.e., amount sweet products, amount of gold products and number of kids residing in home. Now, in order to obtain a better regression, we excluded all the variables with a low impact and included just the ones with high potential, always basing our selection on p-values. We didn’t get rid of the variable of if a customer accepted the campaign: although we had a few observations that didn’t seem to be statistically important, we thought it wouldn’t make sense to eliminate this bunch of variables.
Label: Web Purchases, Features: Numerical Variables selected based on findings in Part a
# Train the model
better_model <- lm(NumWebPurchases ~ Income + Kidhome + Teenhome + MntWines + MntSweetProducts + MntGoldProds + NumDealsPurchases +NumStorePurchases +NumWebVisitsMonth, data=mark_train)
summary(model)##
## Call:
## lm(formula = NumWebPurchases ~ ., data = mark_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.0304 -0.9534 -0.0906 0.8789 22.7327
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.684e+00 1.026e+01 -0.262 0.7936
## ID 3.522e-06 1.460e-05 0.241 0.8095
## Year_Birth -4.914e-03 4.523e-03 -1.086 0.2775
## EducationBasic -2.833e-01 3.381e-01 -0.838 0.4021
## EducationGraduation 1.947e-02 1.718e-01 0.113 0.9098
## EducationMaster -5.907e-02 2.019e-01 -0.293 0.7698
## EducationPhD 1.810e-01 1.996e-01 0.907 0.3647
## Marital_StatusAlone 3.422e+00 2.007e+00 1.705 0.0885 .
## Marital_StatusDivorced 1.503e+00 1.439e+00 1.045 0.2962
## Marital_StatusMarried 1.613e+00 1.432e+00 1.126 0.2602
## Marital_StatusSingle 1.557e+00 1.434e+00 1.085 0.2780
## Marital_StatusTogether 1.589e+00 1.433e+00 1.108 0.2679
## Marital_StatusWidow 1.573e+00 1.453e+00 1.082 0.2792
## Marital_StatusYOLO 2.480e+00 2.010e+00 1.234 0.2174
## Income 3.228e-05 4.393e-06 7.347 3.10e-13 ***
## Kidhome -8.052e-01 1.200e-01 -6.712 2.59e-11 ***
## Teenhome 1.419e-01 1.128e-01 1.258 0.2086
## Dt_Customer 5.719e-04 2.732e-04 2.093 0.0365 *
## Recency -2.037e-04 1.694e-03 -0.120 0.9043
## MntWines 2.123e-03 2.622e-04 8.094 1.08e-15 ***
## MntFruits 1.019e-03 1.672e-03 0.609 0.5423
## MntMeatProducts -8.667e-04 3.769e-04 -2.299 0.0216 *
## MntFishProducts 8.706e-04 1.296e-03 0.672 0.5018
## MntSweetProducts 7.728e-03 1.634e-03 4.729 2.43e-06 ***
## MntGoldProds 9.768e-03 1.132e-03 8.627 < 2e-16 ***
## NumDealsPurchases 2.122e-01 3.156e-02 6.724 2.39e-11 ***
## NumCatalogPurchases -2.324e-02 2.865e-02 -0.811 0.4175
## NumStorePurchases 1.271e-01 2.292e-02 5.545 3.40e-08 ***
## NumWebVisitsMonth 3.685e-01 3.251e-02 11.336 < 2e-16 ***
## AcceptedCmp3 1.227e-01 1.951e-01 0.629 0.5294
## AcceptedCmp4 7.614e-02 2.237e-01 0.340 0.7337
## AcceptedCmp5 -5.279e-01 2.359e-01 -2.237 0.0254 *
## AcceptedCmp1 -9.955e-02 2.273e-01 -0.438 0.6615
## AcceptedCmp2 -1.853e+00 4.278e-01 -4.332 1.57e-05 ***
## Complain 5.923e-01 4.858e-01 1.219 0.2229
## Z_CostContact NA NA NA NA
## Z_Revenue NA NA NA NA
## Response 2.775e-01 1.641e-01 1.691 0.0911 .
## CountryCA 9.053e-02 2.268e-01 0.399 0.6898
## CountryGER -4.932e-02 2.748e-01 -0.180 0.8576
## CountryIND 8.051e-02 2.648e-01 0.304 0.7611
## CountryME -5.631e-01 1.422e+00 -0.396 0.6922
## CountrySA 2.223e-02 2.197e-01 0.101 0.9194
## CountrySP -3.258e-02 1.946e-01 -0.167 0.8671
## CountryUS 8.944e-02 2.794e-01 0.320 0.7490
## actual_age NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.978 on 1729 degrees of freedom
## Multiple R-squared: 0.5032, Adjusted R-squared: 0.4911
## F-statistic: 41.69 on 42 and 1729 DF, p-value: < 2.2e-16# Get residuals
res <- resid(better_model)
# Produce residual vs. fitted plot
plot(fitted(better_model), res, main="Residual vs. fitted Values", xlab='Fitted', ylab='Residuals')
#qqnorm(res)
# Let the model predict values for the test data
pred <- predict(better_model,mark_test)
# Visualize the models performance
plot(pred,mark_test$NumWebPurchases, main="Predicted vs. actual Values", xlab='Predicted Values', ylab='Actual Values')
The second regression we run has similar R^2 and R^2 adjusted compared to the first one but this doesn’t mean the model hasn’t improved. In fact, we are now dealing with a lower number of variables. The F-statistic has raised from 41 to 183, meaning we gathered strong evidence that these variables are all statistically influential and that there’s a very low chance they have a value of 0. We imagined that when we would have used fewer models in our regression that it would get stronger but that was not the case for us. This could be due to significance the isolated variables can have throughout our entire analysis.
Observing the estimates values from our multiple (better) regression we can select the variables that impact the most, positively, or negatively, on the case. For example, the number of kids at home is the voice that has the most negative impact we know that the odds of business success will decrease by 45% (exp (-6.06 e-01 -1) %) for each kid at home in our data seems to impact negatively on the odds of web purchases. On the other hand, the number of teens at home and number of web visits per home have the highest positive influence in the regression. These variables bring respectively 43 % (exp (3.562 e-01)-1) and 42% (exp (3.530 e-01) -1) increase in the odds of business success for each unit increased of both these voices. Last, the variable with the least impact in the model ends up being Income, as it’s the closest to zero. It just brings 0.0032% (exp (3.175 e-05) -1) positive influence. Our recommendation would be of course to focus on the voices with the best positive impact and try to avoid as much as possible the ones lowering the odds of business success. For example, it’s important to keep in mind that targeting people with kids at home seems to be negative, on the other hand, it could be convenient to rely on people with teenagers at home, probably because teens are more willing than kids to use online sources. Moreover, the CMO should focus more on the number of web visits which has a 42% (exp (3.530 e-01) -1) positive impact. This could be done by allocating some budget on online ads or on everything that could bring visibility to the website. Speaking about budget allocation, it would be crucial to understand how to allocate and thus to select the variables with a low influence in the model. For example, our team thinks Income wouldn’t be such a critical aspect to focus on, the company should save resources by not targeting this market segment and maybe invest in others with better potential. In fact, besides Income, there are a few more variables that seem to have higher importance but still not as significant to justify an effort. Amount spent on wines, gold, and sweet products in the past two years estimates, for instance, are close to zero, and despite their relevance, could also not be taken into consideration in the context of budget allocation. In conclusion, as we have seen, even though the better regression includes just the most significant variables, it seems that from a statistical point of view just the variation of a few manage to change the odds of business success and so the output of a business reasoning.
We should also use the “income” variable to see the different type of classes they have within their data set. We should break this category into three tiers: low-income, middle-income, and high-income brackets to acquire more specific information for targeting campaigns to the varying income classes that are useful in this data set. As the two campaigns did very well and were closely related to in-store purchases we need to see the specifics of why these campaigns were successful. On the other hand, we need to investigate the failure of purchases from the other campaigns and lean away from repeating these tactics again to not be wasteful in budgeting and marketing resources.
We also highly advise personalizing the communications, deals, and preferences with more specific characteristics for personnel. I would say that more specific marketing should be based on a customer demographic: education level, marital status, income level, and age, for example, to have a more pinpointed approach with advertising. This could be evident with doing more in-depth analysis of why one campaign was more successful to another, based on who saw it and made a purchase. We need to investigate performing A/B testing to increase deals. Using resources more effectively with data analysis leads to efficient marketing tactics for increased Return on investment for marketing budgets.
We intend to find if there is any significance in demographics with purchasing behavior between the U.S. and the rest of the world. This is extremely vital for the marketing department due to attaining an overview of which region in the world purchases more and how to more effectively allocate budget and effort with different strategies.
Our approach was to aggregate the columns which included: purchases made in store, catalog purchases, purchases through deals, and web purchases. From there, we created dummy variables with 1 and 0. If the country was US, we would place a 1, and if not, we would input a 0. We compared the two different categories with our explanatory variable of using the dummy variable for the country and our response will be the total purchases. To find out statistical significance, we will calculate the 95% interval for the difference in means.
H0: U.S. Total Purchases <= Rest of the world Ha: U.S. Total Purchases > Rest of the world
# Aggregate total purchases
cat_num_cols$Total_Purch <- cat_num_cols$NumDealsPurchases + cat_num_cols$NumWebPurchases + cat_num_cols$NumCatalogPurchases + cat_num_cols$NumStorePurchases
# Create sub df for analysis for US customers and Rest of world
us_customer <- cat_num_cols[which(cat_num_cols$Country_US==1),]
world_customer <- cat_num_cols[-which(cat_num_cols$Country_US==1),]
# Observations & Mean of purchases & Standard deviation of US-Customer
n_us <- nrow(us_customer)
mean_us <- mean(us_customer$Total_Purch)
sd_us <- sd(us_customer$Total_Purch)
# Observations & Mean of purchases & Standard deviation of World
n_world <- nrow(world_customer)
mean_world <- mean(world_customer$Total_Purch)
sd_world <- sd(world_customer$Total_Purch)
# Calculate pooled variance
sp = ((n_us-1)*sd_us^2 + (n_world-1)*sd_world^2) / (n_us+n_world-2)
# Calculate margin of error
margin <- qt(0.975,df=n_us+n_world-1)*sqrt(sp/n_us + sp/n_world)
#calculate lower and upper bounds of confidence interval
low <- (mean_world-mean_us) - margin
high <- (mean_world-mean_us) + marginThe 95% confidence interval for the true difference in population means is between -2.9700892 and 0.0094266
We saw that the U.S. and the rest of the world are not significantly different in terms of total purchases made. This was made through calculating the confidence intervals of the difference in means for the two different categories. Because our 95% confident interval included 0, we were able to see that there was no significant difference between total purchases made in the U.S. and the rest of the world. With the included box plot visual, you can see that they are very similar to each other with its boxplot graphing.
library(plotly)
fig <- plot_ly(y = us_customer$Total_Purch, type = "box", name="US") %>%
layout(title = 'Average Purchases per Country', plot_bgcolor = "#e5ecf6",xaxis = list(title = 'Country'),yaxis = list(title = 'Average Purchases'))
fig <- fig %>% add_trace(y = world_customer$Total_Purch, name='Rest of world')
figggplot(data=mark_test,aes(x=MntWines, y=MntMeatProducts))+ geom_point()
Our motivation with this analysis is to see if people who bought gold above the average purchase in store more often those who did not. This information could prove useful to categorize customers into different labels for seeing the projection of customers buying habits.
#calculating the mean for gold purchasers and store purchasers
MntGoldProds.mean <- mean (cat_num_cols$MntGoldProds)
MntGoldProds.std <- sd (cat_num_cols$MntGoldProds)
NumStorePurchases.mean <- mean (cat_num_cols$NumStorePurchases)
NumStorePurchases.std <- sd (cat_num_cols$NumStorePurchases)
print (MntGoldProds.mean)## [1] 43.96525print (NumStorePurchases.mean)## [1] 5.800993for (i in 1:nrow(cat_num_cols)){
if (cat_num_cols$MntGoldProds[i] > MntGoldProds.mean){
if (cat_num_cols$NumStorePurchases[i] > NumStorePurchases.mean){
cat_num_cols$test[i] <- c("CONSERVATORY")} else {cat_num_cols$test[i] <- c("TECH")
}
} else {cat_num_cols$test[i] <- c("LOWER")}
}
which (cat_num_cols$test == 'LOWER') ## [1] 2 3 4 5 6 7 8 9 10 11 12 13 14 16
## [15] 17 19 21 22 23 25 26 27 28 29 31 32 33 34
## [29] 35 36 37 38 40 41 42 45 46 47 48 49 53 54
## [43] 55 57 59 60 61 62 63 66 68 69 70 71 73 75
## [57] 76 77 80 81 82 84 85 86 87 89 91 92 93 96
## [71] 97 98 100 101 105 107 108 110 111 113 114 115 119 120
## [85] 122 124 125 126 127 128 129 130 133 136 137 138 139 140
## [99] 141 143 145 146 147 148 150 151 152 153 154 155 156 157
## [113] 159 160 161 162 163 165 167 168 170 171 173 174 175 176
## [127] 178 179 180 182 183 184 185 186 190 194 195 196 197 198
## [141] 200 201 202 206 207 209 210 211 212 213 215 218 219 220
## [155] 221 222 223 224 225 226 227 228 229 230 231 232 235 240
## [169] 241 243 244 245 246 247 248 249 250 251 252 253 254 255
## [183] 259 261 262 263 264 265 267 270 272 274 275 278 280 281
## [197] 283 284 285 286 287 288 289 290 291 292 294 296 297 298
## [211] 299 300 301 302 303 304 305 307 309 310 312 314 315 316
## [225] 317 318 319 320 321 322 324 325 328 329 330 331 332 334
## [239] 336 337 338 340 341 342 343 344 346 347 349 350 351 354
## [253] 355 357 358 359 360 362 363 364 365 367 368 369 371 372
## [267] 373 376 379 381 384 385 386 387 388 389 390 392 393 394
## [281] 395 396 397 398 399 400 401 403 404 405 407 408 409 410
## [295] 412 413 414 416 418 422 423 424 425 426 427 428 429 430
## [309] 431 432 433 434 435 436 437 438 439 440 441 442 443 446
## [323] 447 450 452 453 454 455 456 459 460 461 462 464 465 468
## [337] 469 471 472 473 476 477 479 480 482 483 484 485 486 487
## [351] 488 489 492 494 496 498 500 503 504 505 507 508 513 514
## [365] 515 520 521 522 523 524 525 526 527 528 529 531 533 535
## [379] 536 537 538 539 540 541 543 546 547 548 549 550 552 553
## [393] 554 555 556 557 559 560 561 562 563 564 565 566 567 568
## [407] 570 572 573 575 576 577 578 579 581 582 583 584 585 586
## [421] 587 588 592 594 595 596 597 598 599 600 601 602 603 604
## [435] 605 606 607 608 609 610 611 613 615 616 617 618 621 623
## [449] 629 630 633 635 638 639 640 641 642 643 644 645 648 649
## [463] 651 652 653 654 655 656 657 658 662 663 664 666 667 671
## [477] 674 675 676 678 681 682 683 684 685 686 687 688 689 691
## [491] 692 694 695 697 698 700 702 705 707 708 710 712 713 716
## [505] 718 719 721 722 723 725 726 728 729 730 731 732 734 736
## [519] 739 744 746 747 748 749 750 751 761 762 763 764 765 766
## [533] 768 769 770 771 772 773 774 777 779 780 781 783 784 786
## [547] 787 788 789 791 792 793 794 798 799 800 802 803 804 807
## [561] 808 809 810 811 812 813 814 815 817 818 820 821 822 826
## [575] 828 829 830 831 836 837 839 841 842 844 846 847 848 849
## [589] 850 853 854 855 857 858 859 860 861 863 865 867 868 869
## [603] 870 871 872 874 875 878 881 882 886 887 889 890 892 894
## [617] 895 896 897 898 899 901 902 904 905 907 908 910 911 912
## [631] 918 919 921 923 924 925 927 932 933 936 938 939 940 941
## [645] 942 943 946 949 950 951 952 955 956 959 960 961 962 963
## [659] 964 965 966 968 969 970 973 974 977 979 980 982 983 984
## [673] 985 988 989 991 992 993 996 997 999 1000 1001 1002 1003 1005
## [687] 1006 1007 1008 1009 1011 1012 1014 1015 1016 1017 1018 1019 1020 1021
## [701] 1024 1025 1026 1029 1030 1032 1033 1034 1035 1037 1039 1041 1042 1043
## [715] 1044 1045 1048 1050 1051 1052 1053 1056 1057 1058 1061 1065 1066 1067
## [729] 1069 1071 1074 1076 1077 1078 1079 1081 1082 1083 1084 1085 1086 1087
## [743] 1088 1091 1092 1093 1094 1095 1096 1097 1098 1100 1101 1102 1104 1107
## [757] 1108 1109 1111 1112 1113 1115 1116 1119 1120 1122 1123 1124 1125 1126
## [771] 1127 1129 1130 1131 1133 1134 1135 1136 1137 1138 1139 1140 1142 1143
## [785] 1144 1145 1146 1148 1149 1150 1152 1153 1154 1155 1158 1159 1160 1162
## [799] 1163 1164 1165 1166 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177
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## Country_SP Country_US ID Year_Birth Income Kidhome Teenhome Recency
## 2 0 1 2174 1954 46344 1 1 38
## 3 0 1 4141 1965 71613 0 0 26
## 4 0 1 6182 1984 26646 1 0 26
## 5 0 1 5324 1981 58293 1 0 94
## 6 0 1 7446 1967 62513 0 1 16
## 7 0 1 965 1971 55635 0 1 34
## 8 0 1 6177 1985 33454 1 0 32
## 9 0 1 4855 1974 30351 1 0 19
## 10 0 1 5899 1950 5648 1 1 68
## 11 0 1 387 1976 7500 0 0 59
## 12 0 1 2125 1959 63033 0 0 82
## 13 0 1 8180 1952 59354 1 1 53
## 14 0 1 2569 1987 17323 0 0 38
## 16 0 1 9736 1980 41850 1 1 51
## 17 0 1 4939 1946 37760 0 0 20
## 19 0 1 2278 1985 33812 1 0 86
## 21 0 1 5376 1979 2447 1 0 42
## 22 0 1 1993 1949 58607 0 1 63
## 23 0 1 4047 1954 65324 0 1 0
## 25 0 1 7892 1969 18589 0 0 89
## 26 0 1 2404 1976 53359 1 1 4
## 27 0 0 9422 1989 38360 1 0 26
## 28 0 0 1966 1965 84618 0 0 96
## 29 0 0 6864 1989 10979 0 0 34
## 31 0 0 5710 1970 40548 0 1 31
## 32 0 0 7373 1952 46610 0 2 8
## 33 0 0 8755 1946 68657 0 0 4
## 34 0 0 10738 1951 49389 1 1 55
## 35 0 0 4339 1970 67353 0 1 37
## 36 0 0 10755 1976 23718 1 0 76
## 37 0 0 8595 1973 42429 0 1 99
## 38 0 0 2968 1943 48948 0 0 53
## 40 0 0 503 1985 20559 1 0 88
## 41 0 0 8430 1957 21994 0 1 4
## 42 0 0 2139 1975 7500 1 0 19
## 45 0 0 7286 1968 41728 1 0 92
## 46 0 0 6566 1954 72550 1 1 39
## 47 0 0 8614 1957 65486 0 1 29
## 48 0 0 4114 1964 79143 0 0 2
## 49 0 0 1331 1977 35790 1 0 54
## 53 0 0 10383 1966 70287 0 0 30
## 54 0 0 7437 1988 27938 1 0 31
## 55 0 0 8375 1968 55954 0 1 63
## 57 0 0 3076 1975 66653 1 1 18
## 59 0 0 6518 1951 67680 0 1 8
## 60 0 0 4137 1948 70666 0 0 29
## 61 0 0 8082 1971 25721 1 0 75
## 62 0 0 1386 1967 32474 1 1 0
## 63 0 0 9369 1979 88194 0 1 19
## 66 0 0 1402 1954 66991 0 0 1
## 68 0 0 8132 1975 60631 1 1 88
## 69 0 0 1050 1952 28332 0 0 58
## 70 0 0 5846 1977 40246 1 0 68
## 71 0 0 1859 1972 75251 0 0 34
## 73 0 0 10401 1976 26326 0 0 12
## 75 0 0 3332 1985 29760 1 0 87
## 76 0 0 2261 1969 26304 1 0 5
## 77 0 0 5346 1973 23559 1 0 87
## 80 0 0 5268 1960 29440 1 1 95
## 81 0 0 273 1970 36138 1 1 48
## 82 0 0 4452 1957 50388 0 1 3
## 84 0 0 771 1967 54178 0 1 79
## 85 0 0 11178 1972 42394 1 0 69
## 86 0 0 2230 1970 23626 1 0 84
## 87 0 0 7516 1983 30096 1 0 30
## 89 0 0 11100 1972 51813 1 1 37
## 91 0 0 3037 1983 50150 0 0 32
## 92 0 0 1473 1960 47823 0 1 0
## 93 0 0 1016 1959 34554 0 1 43
## 96 0 0 8908 1959 87195 0 0 35
## 97 0 0 8373 1979 24594 1 0 94
## 98 0 0 7533 1964 49096 1 1 15
## 100 0 0 3629 1978 38557 1 0 17
## 101 0 0 10991 1960 89058 0 0 18
## 105 0 0 999 1991 86037 0 0 95
## 107 0 0 2286 1962 50785 1 1 27
## 108 0 0 1592 1970 90765 0 0 25
## 110 0 0 5234 1967 30753 1 1 85
## 111 0 0 9862 1969 21918 1 0 37
## 113 0 0 6205 1967 32557 1 0 13
## 114 0 0 8442 1985 19510 2 0 63
## 115 0 0 7386 1976 30992 1 0 83
## 119 0 0 3381 1953 54348 1 1 51
## 120 0 0 11051 1956 77376 1 1 72
## 122 0 0 433 1958 61331 1 1 42
## 124 0 0 5290 1964 41551 1 1 51
## 125 0 0 1685 1967 62981 0 0 21
## 126 0 0 5342 1976 9548 1 0 31
## 127 0 0 9579 1959 33762 2 1 61
## 128 0 0 7660 1973 35860 1 1 37
## 129 0 0 1461 1965 36921 1 1 74
## 130 0 0 821 1992 92859 0 0 46
## 133 0 0 2937 1974 68352 0 1 47
## 136 0 0 10837 1975 23957 1 0 47
## 137 0 0 3120 1981 38547 1 0 49
## 138 0 0 5885 1973 35688 2 1 94
## 139 0 0 4050 1966 49605 0 0 65
## 140 0 0 3830 1953 59354 0 2 59
## 141 0 0 7987 1969 65747 0 1 96
## 143 0 0 92 1988 34176 1 0 12
## 145 0 0 10240 1949 69372 0 0 10
## 146 0 0 3025 1958 49967 0 1 4
## 147 0 0 9283 1978 60199 1 2 49
## 148 0 0 9970 1977 55375 0 1 3
## 150 0 0 2795 1958 30523 2 1 0
## 151 0 0 9260 1945 70356 0 0 20
## 152 0 0 8182 1984 23228 1 0 91
## 153 0 0 10095 1975 74165 0 0 9
## 154 0 0 8475 1973 157243 0 1 98
## 155 0 0 4370 1966 43482 2 1 83
## 156 0 0 10573 1953 62551 0 0 27
## 157 0 0 3712 1959 52332 0 0 63
## 159 0 0 3152 1957 26091 1 1 84
## 160 0 0 5610 1965 33456 1 1 58
## 161 0 0 6460 1982 28718 1 0 78
## 162 0 0 1349 1970 50447 2 0 4
## 163 0 0 1880 1959 53537 1 1 17
## 165 0 0 8717 1975 80427 0 1 56
## 167 0 0 10854 1970 38853 1 1 90
## 168 0 0 10492 1959 38285 2 1 96
## 170 0 0 7698 1976 51650 0 1 81
## 171 0 0 3559 1988 16248 1 0 77
## 173 0 0 10796 1984 30477 1 0 16
## 174 0 0 2563 1961 28249 0 0 80
## 175 0 0 3518 1983 25271 1 0 45
## 176 0 0 5823 1970 32303 0 1 63
## 178 0 0 6982 1976 74068 0 0 14
## 179 0 0 6431 1964 45759 1 1 13
## 180 0 0 9733 1978 24882 1 0 52
## 182 0 0 7829 1900 36640 1 0 99
## 183 0 0 1324 1988 38872 1 0 93
## 184 0 0 326 1973 51148 1 1 38
## 185 0 0 7224 1977 31353 1 1 24
## 186 0 0 6999 1980 69661 0 0 80
## 190 0 0 7300 1952 69142 0 1 50
## 194 0 0 1030 1965 40637 1 1 72
## 195 0 0 624 1984 18890 0 0 5
## 196 0 0 5176 1988 29604 1 0 88
## 197 0 0 5552 1963 48721 1 1 50
## 198 0 0 868 1966 44794 0 1 99
## 200 0 0 2677 1956 46097 0 1 11
## 201 0 0 6521 1954 77972 0 0 18
## 202 0 0 6927 1968 44377 1 1 70
## 206 0 0 7264 1978 52195 2 1 2
## 207 0 0 6274 1948 83790 0 0 81
## 209 0 0 454 1980 69508 1 0 48
## 210 0 0 1755 1988 45204 1 0 93
## 211 0 0 2075 1969 72460 0 0 56
## 212 0 0 4785 1970 77622 0 2 3
## 213 0 0 9076 1980 30732 1 0 44
## 215 0 0 4653 1978 42011 1 0 46
## 218 0 0 460 1972 79930 0 0 72
## 219 0 0 3726 1986 34320 1 0 66
## 220 0 0 6798 1966 37070 1 1 30
## 221 0 0 5341 1962 81975 0 1 2
## 222 0 0 1606 1971 38590 1 0 65
## 223 0 0 9617 1951 15033 0 0 37
## 224 0 0 3233 1965 62745 0 1 89
## 225 0 0 89 1975 22212 1 0 49
## 226 0 0 2936 1982 23661 1 0 46
## 227 0 0 9449 1958 79761 0 1 32
## 228 0 0 7297 1973 7500 1 0 54
## 229 0 0 11004 1893 60182 0 1 23
## 230 0 0 313 1968 73455 0 0 98
## 231 0 0 9940 1958 64961 0 1 97
## 232 0 0 10735 1971 22804 1 0 75
## 235 0 0 7411 1964 31686 1 1 31
## 240 0 0 10236 1975 31160 1 0 59
## 241 0 0 6340 1985 29938 1 0 52
## 243 0 0 2227 1958 26490 0 0 92
## 244 0 0 10992 1967 75702 1 1 77
## 245 0 0 5304 1974 30899 1 0 35
## 246 0 0 309 1961 63342 0 1 48
## 247 0 0 3406 1964 45989 0 1 97
## 248 0 0 2661 1964 18701 1 1 95
## 249 0 0 3153 1957 40737 2 1 24
## 250 0 0 10264 1979 15287 1 0 60
## 251 0 0 1184 1968 69674 0 2 46
## 252 0 0 4200 1970 44159 1 0 85
## 253 0 0 9396 1978 37717 1 0 31
## 254 0 0 2387 1952 43776 1 1 9
## 255 0 0 6690 1966 38179 1 1 30
## 259 0 0 4557 1970 22070 1 0 11
## 261 0 0 5726 1983 31788 1 0 15
## 262 0 0 3066 1975 61905 0 1 2
## 263 0 0 6336 1960 29315 1 1 55
## 264 0 0 10967 1963 33378 1 1 38
## 265 0 0 2304 1963 66313 0 1 86
## 267 0 0 7828 1981 77882 0 0 29
## 270 0 0 4669 1981 24480 1 0 46
## 272 0 0 8652 1946 37760 0 0 20
## 274 0 0 8962 1975 44319 1 1 18
## 275 0 0 9974 1974 30631 1 0 75
## 278 0 0 10102 1966 79946 0 0 19
## 280 0 0 9262 1984 32414 0 0 11
## 281 0 0 7254 1969 38361 1 0 74
## 283 0 0 4927 1971 16626 2 0 76
## 284 0 0 10451 1965 29672 1 1 6
## 285 0 0 2926 1952 55951 0 1 62
## 286 0 0 2874 1988 35388 1 0 20
## 287 0 0 9738 1986 42386 1 0 43
## 288 0 0 7119 1982 68627 0 0 45
## 289 0 0 3924 1965 57912 0 1 34
## 290 0 0 5827 1958 35246 1 1 53
## 291 0 0 5596 1956 58821 0 1 44
## 292 0 0 10812 1978 46377 1 0 89
## 294 0 0 9491 1984 23976 1 0 68
## 296 0 0 6609 1966 27038 0 0 64
## 297 0 0 6963 1947 77457 0 0 85
## 298 0 0 4440 1964 64100 0 1 93
## 299 0 0 1379 1992 42670 0 0 12
## 300 0 0 10032 1976 12571 1 0 86
## 301 1 0 2826 1967 22574 2 1 28
## 302 1 0 6320 1976 70893 0 0 42
## 303 1 0 3050 1966 54198 1 1 13
## 304 1 0 231 1956 28839 1 1 86
## 305 1 0 2392 1972 40321 1 1 59
## 307 1 0 113 1951 30833 1 1 33
## 309 1 0 520 1974 34421 1 0 81
## 310 1 0 8275 1965 47025 1 1 6
## 312 1 0 2607 1953 40464 0 1 78
## 314 1 0 5835 1976 14849 1 0 39
## 315 1 0 3896 1984 27255 1 0 3
## 316 1 0 10350 1950 54432 2 1 37
## 317 1 0 7786 1986 29999 1 0 22
## 318 1 0 2861 1983 24072 1 0 79
## 319 1 0 663 1989 33996 0 0 46
## 320 1 0 10675 1956 66334 0 1 82
## 321 1 0 2521 1971 35178 1 0 10
## 322 1 0 9592 1983 22010 1 0 51
## 324 1 0 7381 1968 75693 0 0 10
## 325 1 0 4099 1972 30675 1 0 14
## 328 1 0 10172 1988 68655 0 0 95
## 329 1 0 11191 1986 41411 0 0 11
## 330 1 0 9707 1969 55212 0 1 65
## 331 1 0 6245 1950 59292 0 1 71
## 332 1 0 8234 1973 27190 1 0 13
## 334 1 0 8553 1965 44300 1 1 65
## 336 1 0 8842 1971 30372 1 1 33
## 337 1 0 11030 1976 33181 1 0 90
## 338 1 0 2579 1957 71113 0 1 95
## 340 1 0 9185 1966 69759 0 0 38
## 341 1 0 11056 1974 72099 0 0 18
## 342 1 0 9365 1976 60000 0 1 57
## 343 1 0 6201 1978 38643 1 1 45
## 344 1 0 2061 1955 50737 0 1 61
## 346 1 0 6142 1943 65073 0 0 65
## 347 1 0 4107 1948 46681 0 2 52
## 349 1 0 9336 1969 62187 0 1 38
## 350 1 0 2958 1978 28442 2 0 53
## 351 1 0 933 1978 37717 1 0 31
## 354 1 0 5125 1960 79530 0 0 64
## 355 1 0 4432 1976 31615 1 0 82
## 357 1 0 2928 1975 52614 0 1 63
## 358 1 0 5680 1984 35684 1 0 66
## 359 1 0 10313 1975 48178 1 1 69
## 360 1 0 2557 1970 29548 1 0 52
## 362 1 0 1519 1972 38578 1 1 2
## 363 1 0 10703 1975 46098 1 1 86
## 364 1 0 1726 1970 22585 0 0 23
## 365 1 0 4459 1989 30279 1 0 13
## 367 1 0 1517 1969 30822 1 0 92
## 368 1 0 2098 1969 33581 2 0 38
## 369 1 0 9543 1985 19986 1 0 74
## 371 1 0 3310 1973 35688 2 1 94
## 372 1 0 2724 1981 36143 1 0 33
## 373 1 0 948 1971 10245 1 0 32
## 376 1 0 2066 1949 38823 0 1 56
## 379 1 0 5642 1979 62499 1 0 0
## 381 1 0 455 1946 51012 0 0 86
## 384 1 0 7414 1971 43824 1 1 18
## 385 1 0 6354 1984 15345 1 0 51
## 386 1 0 798 1987 23442 1 0 71
## 387 1 0 8213 1980 14515 1 0 71
## 388 1 0 2488 1956 31395 1 1 80
## 389 1 0 9999 1965 75276 0 0 2
## 390 1 0 7617 1974 42373 1 1 83
## 392 1 0 2552 1955 55521 1 2 11
## 393 1 0 194 1965 48006 1 1 55
## 394 1 0 3645 1972 27213 1 0 19
## 395 1 0 8890 1971 65808 1 1 1
## 396 1 0 5049 1974 30351 1 0 19
## 397 1 0 9973 1963 50437 0 2 28
## 398 1 0 7301 1982 23616 1 0 76
## 399 1 0 10882 1976 53858 0 1 50
## 400 1 0 7030 1955 66465 0 1 1
## 401 1 0 7422 1987 46923 1 0 90
## 403 1 0 7106 1943 75865 0 0 73
## 404 1 0 6504 1975 19789 1 0 24
## 405 1 0 5067 1994 80134 0 0 11
## 407 1 0 8581 1971 49505 1 1 4
## 408 1 0 5929 1973 37401 1 0 14
## 409 1 0 3129 1983 30096 1 0 30
## 410 1 0 6528 1982 18492 1 0 75
## 412 1 0 6932 1941 93027 0 0 77
## 413 1 0 7284 1953 48686 1 2 8
## 414 1 0 1340 1986 92910 0 0 42
## 416 1 0 5987 1989 10404 1 0 52
## 418 1 0 3725 1961 84865 0 0 1
## 422 1 0 498 1970 30015 1 0 28
## 423 1 0 9347 1956 50943 0 1 49
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## 426 1 0 5455 1971 32011 1 0 89
## 427 1 0 456 1986 7500 1 0 96
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## 436 1 0 2541 1976 24027 1 0 7
## 437 1 0 1142 1953 55707 0 1 91
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## 439 1 0 2807 1969 56796 0 1 24
## 440 1 0 6471 1971 36230 1 0 17
## 441 1 0 6131 1980 70829 1 1 87
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## 443 1 0 4148 1972 38988 1 2 90
## 446 1 0 7540 1952 50300 0 1 52
## 447 1 0 10648 1982 66664 0 0 78
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## 453 1 0 2416 1969 66582 0 0 83
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## 455 1 0 9213 1970 31880 1 0 13
## 456 1 0 9697 1951 53790 0 2 86
## 459 1 0 1581 1980 37406 1 0 18
## 460 1 0 564 1981 56937 1 0 81
## 461 1 0 6619 1978 38415 1 1 91
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## 468 1 0 5300 1973 38961 1 0 60
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## 1893 1 0 5623 1980 31535 1 0 95
## 1894 1 0 5832 1960 92556 0 0 32
## 1896 1 0 6417 1971 80573 0 0 85
## 1900 1 0 2326 1990 18929 0 0 15
## 1901 1 0 4385 1981 36038 1 0 82
## 1902 1 0 2929 1976 20180 0 0 27
## 1903 1 0 10686 1965 34230 1 1 72
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## 1911 1 0 2181 1970 70617 0 0 45
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## 1928 1 0 5015 1976 34529 1 0 94
## 1931 1 0 2495 1974 83891 0 1 24
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## 1933 1 0 9955 1954 39898 0 1 20
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## 1937 1 0 7919 1976 72335 0 0 2
## 1938 1 0 5626 1964 61798 0 0 13
## 1939 1 0 6428 1950 76842 0 0 37
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## 1956 1 0 2452 1971 34600 1 1 8
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## 1963 1 0 10913 1984 31385 1 0 56
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## 1969 1 0 8619 1957 21994 0 1 4
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## 1972 1 0 898 1986 23477 1 0 39
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## 1977 1 0 10164 1958 94472 0 1 75
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## 1981 1 0 10841 1975 56559 0 1 46
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## 1984 1 0 4860 1970 24206 1 0 66
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## 1988 1 0 9923 1972 46423 1 1 6
## 1989 1 0 7181 1977 30368 0 1 97
## 1992 1 0 6870 1959 68805 0 1 73
## 1993 1 0 832 1966 65814 0 1 90
## 1995 1 0 3657 1986 39146 1 0 1
## 1996 1 0 500 1977 96876 0 0 79
## 1997 1 0 1951 1981 34445 0 0 92
## 1998 1 0 3262 1958 68281 0 0 31
## 1999 1 0 4102 1975 37284 1 1 46
## 2000 1 0 7042 1983 36790 0 0 14
## 2001 1 0 10598 1967 27943 1 1 89
## 2002 1 0 255 1951 22263 1 1 31
## 2003 1 0 2375 1972 30261 1 2 75
## 2004 1 0 9829 1978 28587 1 0 42
## 2005 1 0 6214 1982 43815 1 0 53
## 2006 1 0 4954 1956 48195 1 1 53
## 2007 1 0 7861 1952 77027 0 1 23
## 2009 1 0 9423 1979 32765 1 0 49
## 2010 1 0 4198 1965 29672 1 1 6
## 2011 1 0 153 1953 23272 0 0 29
## 2012 0 0 9323 1949 49912 0 1 5
## 2013 1 0 10001 1985 7500 1 0 98
## 2014 1 0 3945 1947 68117 0 1 80
## 2016 0 1 8625 1976 17649 1 0 70
## 2019 0 1 9119 1950 60905 0 1 27
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## 1951 0 0 0 3 11 0
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## 1958 0 0 0 3 11 0
## 1961 0 0 0 3 11 0
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## 1981 0 0 0 3 11 0
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## 1984 0 0 0 3 11 0
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## 1986 0 0 0 3 11 1
## 1987 0 0 0 3 11 0
## 1988 0 0 0 3 11 0
## 1989 0 0 0 3 11 0
## 1992 0 0 0 3 11 0
## 1993 0 0 0 3 11 0
## 1995 0 0 0 3 11 0
## 1996 1 0 0 3 11 1
## 1997 0 0 0 3 11 0
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## 2002 0 0 1 3 11 0
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## 2004 0 0 0 3 11 0
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## 2007 0 0 0 3 11 0
## 2009 0 0 0 3 11 0
## 2010 0 0 0 3 11 0
## 2011 0 0 0 3 11 0
## 2012 0 0 0 3 11 1
## 2013 0 0 0 3 11 0
## 2014 0 0 0 3 11 1
## 2016 0 0 0 3 11 0
## 2019 0 0 0 3 11 0
## 2021 0 0 0 3 11 0
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## 2213 1 0 0 3 11 0
## 2214 0 0 0 3 11 0
## 2216 0 0 0 3 11 1
## actual_age Total_Purch test
## 2 67 6 LOWER
## 3 56 21 LOWER
## 4 37 8 LOWER
## 5 40 19 LOWER
## 6 54 22 LOWER
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## 1402 49 7 LOWER
## 1403 48 9 LOWER
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## 1448 35 5 LOWER
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## 1455 44 12 LOWER
## 1456 55 5 LOWER
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## 1460 65 23 LOWER
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## 1462 34 10 LOWER
## 1463 60 27 LOWER
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## 1471 75 22 LOWER
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## 1474 50 30 LOWER
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## 1476 33 29 LOWER
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## 1482 62 25 LOWER
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## 1488 66 22 LOWER
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## 1492 50 24 LOWER
## 1495 46 11 LOWER
## 1498 44 7 LOWER
## 1501 43 7 LOWER
## 1502 49 7 LOWER
## 1503 51 7 LOWER
## 1506 45 7 LOWER
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## 1508 48 0 LOWER
## 1509 45 32 LOWER
## 1511 63 9 LOWER
## 1513 61 7 LOWER
## 1515 63 17 LOWER
## 1516 46 27 LOWER
## 1517 46 11 LOWER
## 1519 33 17 LOWER
## 1520 32 5 LOWER
## 1522 50 19 LOWER
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## 1527 68 4 LOWER
## 1528 57 24 LOWER
## 1529 35 7 LOWER
## 1530 34 6 LOWER
## 1533 46 4 LOWER
## 1534 70 7 LOWER
## 1536 65 17 LOWER
## 1538 47 32 LOWER
## 1539 54 6 LOWER
## 1540 59 17 LOWER
## 1541 45 4 LOWER
## 1542 60 11 LOWER
## 1544 38 9 LOWER
## 1545 45 21 LOWER
## 1546 33 4 LOWER
## 1547 67 21 LOWER
## 1548 36 8 LOWER
## 1549 69 16 LOWER
## 1550 57 23 LOWER
## 1551 67 23 LOWER
## 1552 71 23 LOWER
## 1555 67 8 LOWER
## 1557 35 5 LOWER
## 1559 61 15 LOWER
## 1560 39 8 LOWER
## 1561 74 18 LOWER
## 1562 29 8 LOWER
## 1563 45 17 LOWER
## 1564 65 4 LOWER
## 1565 74 17 LOWER
## 1566 71 15 LOWER
## 1567 55 8 LOWER
## 1569 48 6 LOWER
## 1570 69 12 LOWER
## 1571 56 12 LOWER
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## 1575 66 13 LOWER
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## 1578 54 7 LOWER
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## 1580 61 12 LOWER
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## 1587 61 6 LOWER
## 1588 60 7 LOWER
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## 1590 44 25 LOWER
## 1591 31 24 LOWER
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## 1598 44 6 LOWER
## 1600 38 4 LOWER
## 1601 34 14 LOWER
## 1602 56 11 LOWER
## 1604 56 8 LOWER
## 1605 50 9 LOWER
## 1606 39 19 LOWER
## 1608 58 13 LOWER
## 1609 69 12 LOWER
## 1610 50 21 LOWER
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## 1613 45 7 LOWER
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## 1616 62 9 LOWER
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## 1618 73 16 LOWER
## 1619 66 26 LOWER
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## 1624 52 5 LOWER
## 1627 42 5 LOWER
## 1629 61 4 LOWER
## 1630 47 16 LOWER
## 1633 71 10 LOWER
## 1634 39 9 LOWER
## 1635 60 22 LOWER
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## 1637 44 28 LOWER
## 1638 74 6 LOWER
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## 1640 67 15 LOWER
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## 1988 49 9 LOWER
## 1989 44 7 LOWER
## 1992 62 12 LOWER
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## 1995 35 11 LOWER
## 1996 44 24 LOWER
## 1997 40 27 LOWER
## 1998 63 25 LOWER
## 1999 46 4 LOWER
## 2000 38 6 LOWER
## 2001 54 8 LOWER
## 2002 70 6 LOWER
## 2003 49 4 LOWER
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## 2005 39 6 LOWER
## 2006 65 17 LOWER
## 2007 69 19 LOWER
## 2009 42 8 LOWER
## 2010 56 4 LOWER
## 2011 68 5 LOWER
## 2012 72 26 LOWER
## 2013 36 10 LOWER
## 2014 74 25 LOWER
## 2016 45 9 LOWER
## 2019 71 15 LOWER
## 2021 70 22 LOWER
## 2022 37 6 LOWER
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## 2213 75 22 LOWER
## 2214 40 19 LOWER
## 2216 67 11 LOWERwhich (cat_num_cols$test == 'CONSERVATORY')## [1] 15 18 43 50 51 52 56 58 64 67 72 74 79 83 88
## [16] 90 94 95 99 102 104 106 112 116 117 118 123 131 132 135
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## [181] 825 832 833 834 835 840 843 845 851 856 862 866 873 877 880
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## [256] 1121 1128 1132 1141 1147 1151 1156 1157 1167 1183 1187 1189 1200 1201 1206
## [271] 1214 1216 1225 1230 1238 1245 1247 1249 1254 1257 1261 1263 1266 1268 1269
## [286] 1271 1276 1277 1285 1292 1293 1296 1297 1304 1308 1310 1313 1320 1321 1322
## [301] 1329 1342 1343 1348 1350 1353 1362 1373 1382 1385 1395 1399 1405 1406 1413
## [316] 1427 1428 1432 1442 1450 1451 1458 1464 1465 1472 1479 1483 1486 1487 1490
## [331] 1493 1494 1496 1497 1500 1504 1505 1510 1514 1521 1523 1525 1526 1532 1535
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## [496] 2166 2171 2172 2179 2181 2189 2195 2197 2199 2203 2204 2205 2215cat_num_cols[which (cat_num_cols$test == 'CONSERVATORY') , ]## Education_2n Cycle Education_Basic Education_Graduation Education_Master
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## Education_PhD Marital_Status_Absurd Marital_Status_Alone
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## Marital_Status_Divorced Marital_Status_Married Marital_Status_Single
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## Marital_Status_Together Marital_Status_Widow Marital_Status_YOLO
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## Country_AUS Country_CA Country_GER Country_IND Country_ME Country_SA
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## Country_SP Country_US ID Year_Birth Income Kidhome Teenhome Recency
## 15 0 1 2114 1946 82800 0 0 23
## 18 0 1 6565 1949 76995 0 1 91
## 43 0 0 1371 1976 79941 0 0 72
## 50 0 0 2225 1977 82582 0 0 54
## 51 0 0 9381 1978 66373 1 1 57
## 52 0 0 6260 1955 82384 0 0 55
## 56 0 0 6853 1982 75777 0 0 12
## 58 0 0 1012 1952 61823 0 1 26
## 64 0 0 4477 1958 69096 0 1 4
## 67 0 0 6312 1959 65031 0 1 29
## 72 0 0 7503 1976 75825 0 0 40
## 74 0 0 1618 1965 56046 0 0 9
## 79 0 0 535 1987 81361 0 0 18
## 83 0 0 8504 1973 79593 0 0 70
## 88 0 0 7247 1960 47916 0 1 72
## 90 0 0 4646 1951 78497 0 0 44
## 94 0 0 4518 1979 85693 0 1 59
## 95 0 0 9370 1945 65846 0 0 68
## 99 0 0 2683 1969 52413 0 2 56
## 102 0 0 5077 1979 77298 0 1 46
## 104 0 0 3267 1963 57288 0 1 27
## 106 0 0 9215 1980 43974 1 0 19
## 112 0 0 9805 1953 56129 0 1 65
## 116 0 0 7215 1983 101970 0 0 69
## 117 0 0 1491 1965 71488 0 0 87
## 118 0 0 11071 1984 79607 0 0 37
## 123 0 0 9597 1969 73448 0 0 10
## 131 0 0 11084 1976 65104 0 1 4
## 132 0 0 8690 1962 86111 0 0 73
## 135 0 0 9949 1976 59809 0 2 36
## 144 0 0 7055 1952 61010 0 1 57
## 149 0 0 2730 1955 80317 0 0 64
## 158 0 0 6637 1988 66951 0 0 46
## 169 0 0 6935 1951 78497 0 0 44
## 172 0 0 10562 1946 66835 0 0 21
## 177 0 0 3934 1966 61286 0 1 34
## 181 0 0 5602 1989 66973 0 0 98
## 187 0 0 9938 1954 80067 0 0 82
## 191 0 0 10424 1968 75922 0 0 60
## 192 0 0 8727 1978 63693 0 1 63
## 193 0 0 2798 1977 102160 0 0 54
## 199 0 0 380 1954 64497 0 1 17
## 203 0 0 2877 1974 46014 1 1 21
## 204 0 0 0 1985 70951 0 0 66
## 214 0 0 7079 1962 63887 0 1 38
## 217 0 0 709 1952 51537 0 1 27
## 233 0 0 9529 1990 73687 0 0 8
## 234 0 0 6439 1958 61074 0 1 37
## 236 0 0 2176 1972 80134 1 0 40
## 237 0 0 9265 1953 75027 0 1 74
## 239 0 0 8932 1969 65176 0 1 57
## 242 0 0 10089 1974 102692 0 0 5
## 256 0 0 1225 1963 80124 0 0 47
## 258 0 0 291 1970 72940 0 0 13
## 266 0 0 3445 1956 60714 0 0 56
## 268 0 0 3762 1986 69867 0 0 30
## 269 0 0 4391 1968 63841 0 1 64
## 273 0 0 1672 1974 65640 0 1 74
## 276 0 0 10983 1952 75278 0 0 17
## 277 0 0 8148 1956 50898 1 1 88
## 282 0 0 4543 1980 82497 0 0 32
## 295 0 0 9274 1961 80950 0 0 44
## 306 1 0 9478 1985 66503 1 0 30
## 308 1 0 1802 1971 64795 0 1 23
## 323 1 0 10151 1960 62204 0 2 38
## 326 1 0 9560 1965 83003 0 0 18
## 335 1 0 1826 1970 84835 0 0 0
## 339 1 0 175 1986 71952 1 0 93
## 345 1 0 6184 1969 68462 0 0 6
## 348 1 0 7274 1957 78618 0 0 87
## 352 1 0 5837 1967 51479 1 1 67
## 353 1 0 10704 1969 54803 0 1 65
## 356 1 0 5955 1962 72025 0 0 27
## 361 1 0 1245 1966 63810 0 1 45
## 366 1 0 1729 1961 66426 0 1 14
## 370 1 0 6878 1954 27421 0 0 14
## 375 1 0 6202 1960 63381 0 1 78
## 377 1 0 9799 1968 83664 1 1 57
## 378 1 0 8318 1979 90300 0 0 7
## 380 1 0 2669 1993 74293 0 0 66
## 382 1 0 7683 1968 70777 0 1 80
## 383 1 0 3428 1991 68682 0 0 56
## 402 1 0 3523 1962 75072 0 1 83
## 406 1 0 4216 1981 91065 0 0 33
## 411 1 0 1361 1974 82584 0 0 26
## 415 1 0 8746 1989 75433 1 0 28
## 417 1 0 3767 1968 61314 0 1 1
## 419 1 0 4324 1989 42387 1 0 42
## 420 1 0 238 1967 67309 1 1 76
## 421 1 0 6694 1964 75236 0 1 27
## 444 1 0 4947 1966 89572 0 0 44
## 448 1 0 1992 1964 60597 0 1 2
## 449 1 0 7851 1949 70165 0 0 60
## 451 1 0 7059 1963 80124 0 0 47
## 457 1 0 3916 1971 49269 1 0 92
## 458 1 0 2021 1975 61456 0 1 47
## 463 1 0 178 1956 62503 0 1 40
## 466 1 0 8970 1972 62010 0 1 93
## 474 1 0 5172 1976 65104 0 1 4
## 475 1 0 5562 1974 60093 0 1 92
## 478 1 0 3403 1958 78952 0 1 11
## 491 1 0 5140 1985 64355 1 0 66
## 493 1 0 4945 1953 53653 0 0 29
## 495 1 0 6071 1989 81217 0 0 77
## 497 1 0 8939 1959 61250 0 1 49
## 499 1 0 4320 1979 60152 0 1 6
## 501 1 0 6036 1959 89120 0 0 78
## 506 1 0 4607 1959 71367 0 0 24
## 509 1 0 2532 1987 73454 0 0 95
## 510 1 0 9214 1991 42691 0 0 48
## 511 1 0 4943 1953 70503 0 0 73
## 517 1 0 3281 1960 49154 1 1 69
## 518 1 0 702 1975 47682 0 1 80
## 532 1 0 7822 1976 54058 0 1 56
## 542 1 0 9504 1949 81698 0 0 4
## 544 1 0 448 1978 54880 1 0 57
## 545 1 0 8314 1982 79908 0 0 30
## 551 1 0 10681 1970 62466 0 1 13
## 580 1 0 4501 1965 69882 0 0 94
## 589 1 0 2453 1960 62807 0 1 83
## 590 1 0 7972 1955 72906 0 0 79
## 591 1 0 895 1948 61467 0 2 69
## 612 1 0 6583 1955 72635 0 0 54
## 614 1 0 10156 1975 84196 0 1 56
## 619 1 0 9624 1958 65488 0 0 46
## 620 1 0 5156 1970 62466 0 1 13
## 622 1 0 8923 1973 83917 0 0 12
## 624 1 0 6945 1952 84574 0 0 72
## 625 1 0 944 1962 56181 0 1 6
## 626 1 0 1907 1950 63120 0 1 53
## 627 1 0 701 1971 73691 0 1 58
## 628 1 0 3856 1960 63381 0 1 78
## 632 1 0 1173 1947 77632 0 0 73
## 634 1 0 4611 1970 105471 0 0 36
## 636 1 0 4679 1977 78710 0 1 42
## 637 1 0 10928 1958 66886 0 1 28
## 646 1 0 3867 1947 73059 0 1 36
## 650 1 0 5084 1975 58330 0 1 87
## 659 1 0 10379 1962 59247 0 2 87
## 660 1 0 9204 1970 66731 0 1 33
## 661 1 0 3197 1980 77353 0 1 38
## 665 0 1 9937 1979 70337 0 0 75
## 669 0 1 4910 1967 68743 0 0 81
## 672 0 1 6327 1967 65777 0 0 87
## 673 0 1 10142 1976 66476 0 1 99
## 690 0 1 6384 1978 71022 0 1 30
## 693 0 0 6988 1986 71952 1 0 93
## 699 0 0 1272 1965 55250 0 1 49
## 701 0 0 1044 1953 58398 0 0 44
## 704 0 0 3619 1960 87305 0 0 19
## 706 0 0 10159 1970 58710 0 1 77
## 709 0 0 4990 1954 59111 0 0 90
## 714 0 0 6086 1955 80395 0 0 62
## 715 0 0 437 1976 75012 0 0 41
## 717 0 0 11101 1991 89891 0 0 17
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## 758 0 0 10582 1979 72063 0 1 3
## 759 0 0 1915 1951 78939 0 0 57
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## 785 0 0 796 1965 60161 0 1 17
## 795 0 0 7352 1957 55267 0 1 28
## 796 0 0 2350 1972 59666 1 1 87
## 797 0 0 9145 1972 72504 0 1 43
## 801 0 0 3732 1955 52750 0 1 72
## 806 0 0 3900 1972 65685 0 1 54
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## 825 0 0 5687 1980 81702 0 0 98
## 832 0 0 5332 1960 82504 0 0 2
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## 834 0 0 10343 1991 61618 0 0 27
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## 840 0 0 4856 1957 82347 0 0 38
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## 845 0 0 425 1985 55357 2 0 66
## 851 0 0 4271 1989 38683 1 0 80
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## 866 0 0 1446 1956 86424 0 0 12
## 873 0 0 4597 1970 82072 0 0 67
## 877 0 0 2930 1978 68092 0 0 7
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## 885 0 0 5046 1958 78331 0 1 97
## 888 0 0 8624 1958 81320 0 0 61
## 891 0 0 2727 1973 57100 0 0 19
## 893 0 0 11074 1977 85072 0 0 94
## 900 0 0 9760 1963 52278 0 1 24
## 903 0 0 9308 1954 62820 0 0 51
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## 909 0 0 4483 1993 72354 0 0 67
## 913 0 0 5871 1979 24401 0 0 98
## 914 0 0 2008 1978 77583 0 0 93
## 915 0 0 3139 1982 74116 0 0 53
## 916 0 0 10037 1993 74293 0 0 66
## 920 0 0 7922 1951 64950 0 1 29
## 926 0 0 3673 1971 55239 0 1 59
## 929 0 0 4128 1962 56067 0 1 51
## 930 0 0 6810 1983 82025 0 0 76
## 934 0 0 1328 1982 57937 0 1 56
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## 976 1 0 9305 1976 79689 0 0 65
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## 1525 0 0 3852 1961 74881 1 1 48
## 1526 0 0 528 1978 65819 0 0 99
## 1532 0 0 591 1957 66033 0 1 76
## 1535 0 0 7725 1965 76800 0 0 33
## 1543 0 0 2793 1976 70179 0 1 10
## 1554 0 0 4261 1946 82800 0 0 23
## 1556 0 0 5350 1991 90638 0 0 29
## 1558 0 0 8685 1977 42014 1 0 56
## 1568 0 0 4608 1987 81361 0 0 18
## 1573 0 0 2612 1987 75794 0 0 33
## 1585 0 0 5453 1956 90226 0 0 26
## 1586 0 0 2565 1955 70638 0 0 69
## 1596 0 0 9242 1990 64509 0 0 19
## 1599 0 0 10099 1984 55434 1 0 21
## 1603 0 0 3298 1956 52973 0 1 92
## 1607 0 0 6097 1959 50664 1 1 64
## 1611 0 0 252 1963 41003 0 0 11
## 1614 0 0 3859 1947 73059 0 1 36
## 1620 0 0 5209 1954 50002 0 1 21
## 1621 0 0 10711 1945 69755 0 0 23
## 1625 0 0 1665 1964 64140 0 2 71
## 1626 0 0 6575 1981 56386 1 1 51
## 1628 0 0 3091 1981 75774 1 0 27
## 1631 0 0 7005 1981 58684 0 0 71
## 1632 0 0 6409 1967 57136 0 0 18
## 1642 0 0 1378 1972 50334 0 1 24
## 1644 0 0 1165 1958 50729 1 1 62
## 1646 0 0 6457 1976 64892 0 1 77
## 1655 1 0 1964 1951 86610 0 0 66
## 1657 1 0 3433 1955 72635 0 0 54
## 1658 1 0 3389 1970 69016 0 1 61
## 1664 1 0 1215 1978 70440 0 0 49
## 1671 1 0 2570 1968 56534 0 1 35
## 1672 1 0 9256 1971 58350 0 1 5
## 1673 1 0 295 1989 81217 0 0 77
## 1674 1 0 7397 1951 49090 0 1 45
## 1675 1 0 6349 1987 61787 0 0 71
## 1683 1 0 4676 1951 73705 0 2 86
## 1684 1 0 8852 1976 61064 0 1 80
## 1692 1 0 7732 1978 64813 1 0 81
## 1694 1 0 477 1986 83512 0 0 31
## 1695 1 0 7832 1959 77520 0 1 30
## 1703 1 0 3498 1976 55424 0 1 6
## 1706 1 0 569 1991 90273 0 0 32
## 1707 1 0 4686 1962 82571 0 0 28
## 1712 1 0 1000 1961 74881 1 1 48
## 1713 1 0 3828 1951 71107 0 1 61
## 1717 1 0 10129 1966 82427 0 0 35
## 1727 1 0 10814 1947 70321 0 0 6
## 1729 1 0 9292 1952 81795 0 0 74
## 1731 1 0 7881 1946 66835 0 0 21
## 1732 1 0 5847 1969 69901 0 1 95
## 1743 1 0 5883 1972 77981 1 0 78
## 1744 1 0 2118 1964 62905 0 1 68
## 1746 1 0 6616 1975 59481 0 1 47
## 1747 1 0 2797 1977 72117 0 1 34
## 1749 1 0 9167 1956 67131 0 1 72
## 1757 1 0 8584 1952 85431 0 0 54
## 1770 1 0 2870 1973 49094 0 1 6
## 1772 1 0 1513 1979 60839 1 1 72
## 1773 1 0 6173 1979 77298 0 1 46
## 1776 1 0 7960 1962 82122 0 0 89
## 1780 1 0 7002 1955 62535 0 1 13
## 1783 1 0 762 1981 75774 1 0 27
## 1784 1 0 2831 1976 78416 0 1 99
## 1789 1 0 9284 1958 53977 0 1 21
## 1795 1 0 269 1963 46757 0 1 71
## 1797 1 0 10513 1992 63207 0 0 68
## 1798 1 0 10177 1954 72071 0 1 4
## 1801 1 0 10057 1951 72282 0 0 70
## 1807 1 0 9166 1968 63967 0 1 57
## 1808 1 0 1440 1978 52513 0 0 84
## 1810 1 0 5441 1965 54111 0 1 97
## 1812 1 0 4687 1958 80739 0 0 92
## 1820 1 0 7849 1970 80336 0 0 12
## 1823 1 0 531 1954 57333 0 1 55
## 1827 1 0 3170 1957 68148 0 0 86
## 1829 1 0 7872 1975 86836 0 0 7
## 1834 1 0 4427 1995 83257 0 0 56
## 1836 1 0 1168 1978 72159 0 0 62
## 1843 1 0 521 1985 54006 1 0 42
## 1848 1 0 9064 1982 69109 0 0 10
## 1849 1 0 3678 1973 60208 1 1 13
## 1853 1 0 2186 1977 82326 0 0 30
## 1855 1 0 8602 1964 69932 0 1 97
## 1860 1 0 3766 1959 33051 0 0 15
## 1861 1 0 1399 1958 72905 0 0 52
## 1862 1 0 1570 1951 69702 0 1 8
## 1863 1 0 3426 1975 70300 1 0 89
## 1867 1 0 6461 1969 62772 0 1 74
## 1875 1 0 3010 1989 88420 0 0 87
## 1889 1 0 544 1954 62820 0 0 51
## 1890 1 0 17 1971 60491 0 1 81
## 1898 1 0 7101 1963 52278 0 1 24
## 1899 1 0 10176 1968 57107 0 1 44
## 1905 1 0 3283 1972 70932 0 1 57
## 1912 1 0 1867 1957 64849 0 0 42
## 1913 1 0 1518 1965 62694 1 1 29
## 1915 1 0 2878 1947 67472 0 1 93
## 1918 1 0 6200 1951 75903 0 1 50
## 1927 1 0 10413 1984 72570 0 0 67
## 1929 1 0 10868 1951 70792 0 0 82
## 1930 1 0 10676 1982 63211 0 0 3
## 1935 1 0 2162 1976 56939 0 1 19
## 1936 1 0 3434 1951 80872 0 0 60
## 1944 1 0 8925 1965 70053 0 1 38
## 1945 1 0 3009 1962 71670 0 0 8
## 1949 1 0 6203 1947 74485 0 0 58
## 1950 1 0 7165 1972 64474 0 1 59
## 1952 1 0 8537 1949 72643 0 0 60
## 1960 1 0 5117 1963 32632 0 0 32
## 1966 1 0 3334 1951 94642 0 0 35
## 1973 1 0 9797 1956 58116 0 1 38
## 1974 1 0 10949 1963 72968 0 0 8
## 1976 1 0 6248 1947 91712 0 0 47
## 1979 1 0 6815 1980 96547 0 0 4
## 1983 1 0 176 1980 67506 0 1 73
## 1990 1 0 3599 1970 63684 0 1 61
## 2008 1 0 4837 1965 71322 0 1 57
## 2015 0 1 3799 1955 67225 0 1 4
## 2017 0 1 3491 1975 55914 0 1 32
## 2020 0 1 4786 1977 75330 1 1 94
## 2023 0 1 5628 1982 61416 0 0 25
## 2024 0 1 5631 1991 49767 0 0 92
## 2025 0 1 4974 1970 83273 1 2 98
## 2028 0 1 4095 1971 37150 1 0 80
## 2035 0 1 4426 1975 55801 1 1 35
## 2041 0 1 1071 1976 70179 0 1 10
## 2042 0 1 10061 1950 59462 0 1 29
## 2044 0 0 2345 1982 70038 0 0 54
## 2057 0 0 10281 1970 64713 1 0 11
## 2062 0 0 5396 1988 68487 0 0 48
## 2064 0 0 2802 1992 87000 0 0 61
## 2071 0 0 7734 1993 79244 0 0 58
## 2074 0 0 7428 1975 80144 0 0 47
## 2076 0 0 1103 1976 81929 1 0 60
## 2085 0 0 340 1970 72967 0 1 1
## 2088 0 0 9729 1955 58275 1 1 48
## 2089 0 0 1008 1981 44953 0 0 31
## 2093 0 0 9907 1969 66476 0 1 80
## 2096 0 0 6262 1962 72217 0 0 93
## 2101 0 0 4915 1956 53230 0 1 86
## 2103 0 0 2088 1972 84906 0 0 98
## 2104 0 0 2493 1966 61286 0 1 34
## 2112 0 0 4369 1957 65487 0 0 48
## 2129 0 0 7233 1972 69209 0 0 4
## 2141 0 0 7313 1949 74859 0 0 63
## 2144 0 0 2429 1954 72071 0 1 4
## 2145 0 0 3520 1990 91172 0 0 94
## 2149 0 0 3968 1970 65706 0 0 77
## 2150 0 0 4084 1975 60934 0 1 41
## 2153 0 0 1772 1975 79174 0 0 2
## 2154 0 0 1544 1967 81380 0 0 51
## 2163 0 0 9723 1960 67716 0 1 8
## 2166 0 0 10652 1957 65487 0 0 48
## 2171 0 0 8722 1957 82347 0 0 38
## 2172 0 0 7118 1957 73803 0 1 61
## 2179 0 0 10968 1969 57731 0 1 0
## 2181 0 0 2736 1978 73807 0 1 88
## 2189 0 0 10469 1981 88325 0 0 42
## 2195 0 0 9589 1948 82032 0 0 54
## 2197 0 0 9246 1985 40101 1 0 73
## 2199 0 0 7366 1982 75777 0 0 12
## 2203 0 0 4749 1968 58554 1 1 55
## 2204 0 0 4478 1979 63777 1 1 87
## 2205 0 0 4201 1962 57967 0 1 39
## 2215 0 0 8235 1956 69245 0 1 8
## MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts
## 15 1006 22 115 59 68
## 18 1012 80 498 0 16
## 43 123 164 266 227 30
## 50 510 120 550 156 40
## 51 328 9 124 12 24
## 52 984 51 432 180 120
## 56 712 26 538 69 13
## 58 523 7 134 37 14
## 64 247 49 159 7 82
## 67 258 107 291 84 37
## 72 1032 105 779 137 105
## 74 577 0 64 0 0
## 79 163 23 424 27 65
## 83 350 60 568 110 133
## 88 505 0 26 0 0
## 90 207 26 447 75 0
## 94 386 172 183 185 132
## 95 562 81 276 80 40
## 99 295 106 271 75 98
## 102 425 115 292 23 35
## 104 527 0 21 0 0
## 106 378 0 101 0 0
## 112 320 48 133 39 72
## 116 722 27 102 44 72
## 117 162 33 124 40 28
## 118 450 133 951 173 0
## 123 236 106 189 23 41
## 131 738 0 232 13 20
## 132 399 28 756 36 40
## 135 598 16 141 32 41
## 144 888 0 57 0 0
## 149 536 11 387 149 91
## 158 277 162 305 173 76
## 169 207 26 447 75 0
## 172 620 26 195 34 17
## 177 356 0 107 19 9
## 181 466 22 432 147 113
## 187 519 17 813 27 173
## 191 265 138 553 224 23
## 192 587 43 337 42 87
## 193 763 29 138 76 176
## 199 1170 48 320 42 32
## 203 410 0 59 19 9
## 204 239 10 554 254 87
## 214 897 23 207 15 11
## 217 787 20 204 0 0
## 233 559 153 503 218 13
## 234 789 0 133 0 28
## 236 1218 16 272 104 0
## 237 925 64 179 133 12
## 239 960 28 183 220 70
## 242 168 148 444 32 172
## 256 483 84 398 205 84
## 258 182 74 298 162 149
## 266 216 162 224 101 92
## 268 196 0 512 33 130
## 269 635 15 100 20 7
## 273 204 7 149 20 15
## 276 304 98 230 150 74
## 277 285 28 242 55 114
## 282 777 129 573 216 21
## 295 525 147 112 219 147
## 306 91 64 128 65 3
## 308 412 5 119 38 29
## 323 317 46 247 151 46
## 326 856 61 570 40 25
## 335 189 104 379 111 189
## 339 656 80 455 52 107
## 345 561 64 785 84 128
## 348 736 163 818 212 163
## 352 247 8 160 6 8
## 353 404 0 92 28 27
## 356 833 33 549 151 133
## 361 977 12 253 16 12
## 366 1043 24 97 32 24
## 370 43 12 96 78 40
## 375 571 50 142 33 50
## 377 866 21 151 28 21
## 378 594 134 786 33 134
## 380 375 152 335 93 91
## 382 554 35 113 0 7
## 383 919 0 505 99 30
## 402 583 34 309 0 22
## 406 822 114 108 179 137
## 411 1076 68 103 29 91
## 415 800 0 297 0 34
## 417 378 0 189 97 172
## 419 235 0 235 19 4
## 420 515 47 181 149 95
## 421 438 66 400 12 38
## 444 606 24 974 197 194
## 448 522 0 257 32 16
## 449 161 0 253 199 191
## 451 483 84 398 205 84
## 457 639 8 166 11 8
## 458 563 76 384 84 192
## 463 478 0 193 110 75
## 466 371 22 39 6 0
## 474 738 0 232 13 20
## 475 502 12 109 16 6
## 478 1170 19 594 99 76
## 491 1047 0 140 67 38
## 493 815 10 239 28 10
## 495 493 70 324 146 40
## 497 382 138 65 60 26
## 499 325 83 300 86 58
## 501 1168 92 408 72 129
## 506 227 23 389 42 21
## 509 982 73 351 56 14
## 510 179 2 64 38 17
## 511 1379 33 216 0 33
## 517 206 10 67 30 27
## 518 162 61 83 97 57
## 532 198 18 252 32 108
## 542 179 28 520 111 123
## 544 308 85 137 102 45
## 545 557 129 761 29 185
## 551 129 129 259 168 121
## 580 292 127 635 132 127
## 589 526 28 135 10 21
## 590 400 32 519 71 75
## 591 410 16 114 0 5
## 612 390 22 323 104 35
## 614 215 63 507 231 31
## 619 603 45 207 36 18
## 620 129 129 259 168 121
## 622 514 22 732 42 198
## 624 387 20 713 38 54
## 625 121 103 69 8 44
## 626 965 69 279 54 41
## 627 707 21 250 85 43
## 628 571 50 142 33 50
## 632 1200 105 758 0 42
## 634 1009 181 104 202 21
## 636 721 0 152 119 50
## 637 315 4 62 41 31
## 646 410 112 420 0 82
## 650 445 53 213 104 98
## 659 327 9 122 19 14
## 660 371 159 194 58 106
## 661 275 59 107 69 101
## 665 187 81 149 25 43
## 669 1132 134 384 175 134
## 672 565 32 435 28 32
## 673 372 18 126 47 48
## 690 909 12 278 0 12
## 693 656 80 455 52 107
## 699 664 58 83 32 0
## 701 299 5 201 21 21
## 704 345 25 501 63 138
## 706 440 81 368 0 122
## 709 524 0 134 28 28
## 714 445 25 706 80 76
## 715 294 142 218 164 58
## 717 412 22 132 59 28
## 720 328 9 124 12 24
## 727 509 133 497 78 24
## 733 901 31 345 75 31
## 735 283 17 372 138 106
## 737 769 80 252 15 34
## 740 445 53 213 104 98
## 741 778 178 689 41 27
## 742 709 93 374 104 80
## 743 359 35 314 93 116
## 745 895 10 101 13 0
## 754 587 51 898 247 0
## 755 595 97 291 127 133
## 757 301 7 74 10 0
## 758 180 32 348 76 32
## 759 794 115 243 150 0
## 760 392 5 91 28 26
## 776 327 9 125 25 4
## 785 584 44 212 46 8
## 795 161 28 136 32 3
## 796 623 53 178 23 17
## 797 604 26 470 123 161
## 801 393 0 239 90 69
## 806 642 14 49 0 7
## 819 351 16 156 7 5
## 823 336 123 274 46 123
## 825 563 50 774 28 31
## 832 362 50 431 134 35
## 833 724 74 929 97 55
## 834 605 91 399 0 45
## 835 547 7 140 0 7
## 840 556 54 845 202 133
## 843 265 199 303 234 9
## 845 374 64 116 84 25
## 851 46 35 69 84 41
## 856 121 62 90 68 20
## 862 562 58 168 43 16
## 866 387 68 569 89 45
## 873 889 55 685 168 92
## 877 852 60 207 78 36
## 880 255 31 446 40 56
## 885 756 138 354 160 169
## 888 183 33 493 59 103
## 891 181 21 69 39 3
## 893 494 92 391 194 11
## 900 953 0 71 0 0
## 903 398 61 265 138 61
## 906 156 29 56 30 32
## 909 315 26 473 220 12
## 913 73 28 217 10 24
## 914 372 23 384 250 12
## 915 871 111 704 145 55
## 916 375 152 335 93 91
## 920 819 0 72 12 0
## 926 371 17 238 23 24
## 929 517 12 54 16 6
## 930 267 98 606 48 70
## 934 261 42 144 55 48
## 937 711 28 142 49 18
## 944 115 27 44 4 146
## 945 263 5 233 69 41
## 948 561 85 171 25 123
## 953 197 107 227 7 59
## 954 693 21 925 31 24
## 957 186 36 234 86 72
## 958 434 137 400 45 137
## 967 729 17 133 11 0
## 971 587 43 337 42 87
## 972 674 168 108 192 42
## 975 1394 22 708 89 91
## 976 311 26 640 180 37
## 978 1296 17 311 45 69
## 981 218 63 282 137 35
## 986 384 25 292 130 41
## 987 244 8 32 7 2
## 994 397 46 288 20 38
## 995 901 31 345 75 31
## 998 1230 0 396 232 178
## 1004 635 114 254 132 152
## 1013 201 53 172 116 77
## 1022 350 60 568 110 133
## 1027 359 0 134 13 26
## 1028 403 24 29 6 24
## 1038 635 114 254 132 152
## 1040 1315 22 780 145 0
## 1046 631 28 491 30 14
## 1047 336 28 282 184 54
## 1049 743 19 181 12 0
## 1054 530 117 678 134 44
## 1055 1215 33 249 64 116
## 1059 736 114 279 82 76
## 1060 473 21 176 19 21
## 1062 288 10 30 4 10
## 1063 1050 178 555 77 138
## 1064 1001 44 107 58 44
## 1072 918 57 842 99 38
## 1080 227 23 389 42 21
## 1089 70 54 109 80 9
## 1114 957 47 494 82 47
## 1117 176 29 818 0 33
## 1118 815 8 53 11 0
## 1121 717 42 864 219 30
## 1128 320 48 133 39 72
## 1132 508 124 249 50 38
## 1141 777 35 731 39 137
## 1147 356 80 329 138 17
## 1151 241 45 604 34 26
## 1156 476 75 162 29 151
## 1157 364 40 425 158 60
## 1167 1156 120 915 94 144
## 1183 635 15 100 20 7
## 1187 341 142 113 259 151
## 1189 221 104 169 102 78
## 1200 625 38 242 50 34
## 1201 972 59 913 25 19
## 1206 919 0 505 99 30
## 1214 618 15 106 20 7
## 1216 318 3 17 4 7
## 1225 997 15 414 99 30
## 1230 411 106 147 54 115
## 1238 882 29 514 38 29
## 1245 151 81 86 168 91
## 1247 736 63 946 219 189
## 1249 982 17 672 23 34
## 1254 489 6 152 8 6
## 1257 630 26 611 44 18
## 1261 464 5 64 7 0
## 1263 1090 12 96 16 12
## 1266 737 21 106 55 63
## 1268 817 185 687 145 55
## 1269 620 54 239 99 98
## 1271 113 61 204 34 26
## 1276 968 147 842 137 42
## 1277 283 112 151 51 72
## 1285 545 7 114 37 21
## 1292 546 72 376 94 145
## 1293 606 7 155 10 0
## 1296 1074 0 69 0 0
## 1297 771 51 154 54 10
## 1304 171 45 73 59 10
## 1308 779 86 537 34 69
## 1310 491 48 231 112 105
## 1313 1205 0 235 19 14
## 1320 711 36 217 172 96
## 1321 774 70 118 182 187
## 1322 163 0 480 0 126
## 1329 262 6 61 0 10
## 1342 823 25 459 124 29
## 1343 231 161 215 171 30
## 1348 368 24 68 38 0
## 1350 160 15 196 15 11
## 1353 543 10 205 160 143
## 1362 689 8 167 11 8
## 1373 709 93 374 104 80
## 1382 635 15 100 20 7
## 1385 642 14 49 0 7
## 1395 1218 16 272 104 0
## 1399 191 56 139 51 8
## 1405 279 172 74 38 8
## 1406 977 12 253 16 12
## 1413 33 87 64 175 16
## 1427 972 19 595 180 26
## 1428 966 168 672 246 105
## 1432 587 54 348 71 54
## 1442 1302 68 731 89 114
## 1450 443 10 75 0 10
## 1451 901 31 345 75 31
## 1458 460 35 422 33 12
## 1464 265 199 303 234 9
## 1465 526 28 135 10 21
## 1472 1449 89 161 69 35
## 1479 247 161 295 210 85
## 1483 758 12 385 33 89
## 1486 244 15 108 4 15
## 1487 539 6 91 8 6
## 1490 306 5 109 21 109
## 1493 620 16 165 0 24
## 1494 434 137 400 45 137
## 1496 811 76 428 99 137
## 1497 423 42 706 73 197
## 1500 675 0 85 10 7
## 1504 910 111 724 49 74
## 1505 252 98 827 219 70
## 1510 332 194 377 149 125
## 1514 1171 43 219 19 14
## 1521 431 82 441 80 20
## 1523 448 40 469 80 0
## 1525 505 72 270 36 27
## 1526 267 38 701 149 165
## 1532 293 35 179 46 59
## 1535 173 26 255 35 71
## 1543 532 88 168 69 44
## 1554 1006 22 115 59 68
## 1556 1156 120 915 94 144
## 1558 244 15 108 4 15
## 1568 163 23 424 27 65
## 1573 754 160 625 63 32
## 1585 1083 108 649 253 151
## 1586 1016 12 215 16 12
## 1596 836 185 575 24 25
## 1599 238 115 215 169 69
## 1603 856 0 76 12 9
## 1607 313 8 104 6 4
## 1611 123 133 142 71 44
## 1614 410 112 420 0 82
## 1620 443 5 71 21 16
## 1621 217 77 373 111 23
## 1625 1459 0 61 0 15
## 1626 230 48 214 13 32
## 1628 340 21 134 258 14
## 1631 479 35 179 28 7
## 1632 267 140 599 34 12
## 1642 284 16 160 84 16
## 1644 239 13 143 45 4
## 1646 527 24 230 32 24
## 1655 446 107 768 33 196
## 1657 390 22 323 104 35
## 1658 726 53 363 123 107
## 1664 690 117 499 76 102
## 1671 691 7 61 10 0
## 1672 493 26 206 116 80
## 1673 493 70 324 146 40
## 1674 494 5 82 7 0
## 1675 621 73 414 78 48
## 1683 612 91 520 258 107
## 1684 387 126 342 0 45
## 1692 293 0 124 179 97
## 1694 1060 61 835 80 20
## 1695 940 44 396 0 88
## 1703 462 61 184 10 53
## 1706 704 129 853 120 74
## 1707 861 31 558 62 95
## 1712 505 72 270 36 27
## 1713 533 10 217 198 174
## 1717 482 147 509 104 107
## 1727 303 23 751 82 26
## 1729 324 132 693 27 118
## 1731 620 26 195 34 17
## 1732 312 21 206 102 92
## 1743 138 120 204 16 126
## 1744 166 75 96 119 107
## 1746 178 3 85 71 66
## 1747 707 20 171 65 60
## 1749 465 71 250 93 35
## 1757 376 53 462 168 53
## 1770 376 0 38 11 8
## 1772 600 21 128 223 150
## 1773 425 115 292 23 35
## 1776 734 22 350 151 25
## 1780 163 48 90 0 45
## 1783 340 21 134 258 14
## 1784 453 38 279 188 38
## 1789 620 16 165 0 24
## 1795 777 30 163 0 51
## 1797 438 169 565 91 169
## 1798 531 69 300 150 138
## 1801 503 27 419 90 139
## 1807 387 84 141 73 35
## 1808 367 17 241 104 188
## 1810 267 6 54 8 3
## 1812 674 92 736 39 0
## 1820 209 19 456 160 142
## 1823 941 14 397 76 58
## 1827 389 66 408 37 57
## 1829 179 21 273 0 21
## 1834 536 27 590 38 107
## 1836 322 53 899 34 40
## 1843 174 77 203 6 24
## 1848 823 25 459 124 29
## 1849 488 23 71 15 0
## 1853 938 142 754 159 81
## 1855 412 172 153 150 105
## 1860 100 71 243 108 94
## 1861 407 114 445 181 165
## 1862 664 9 240 50 19
## 1863 1045 61 338 60 46
## 1867 581 49 157 43 8
## 1875 957 153 612 99 95
## 1889 398 61 265 138 61
## 1890 637 47 237 12 19
## 1898 953 0 71 0 0
## 1899 159 0 120 0 0
## 1905 200 193 100 46 185
## 1912 652 48 350 94 84
## 1913 379 6 157 25 91
## 1915 162 31 127 8 17
## 1918 340 108 185 130 38
## 1927 274 83 216 151 141
## 1929 344 45 654 104 11
## 1930 145 193 459 205 26
## 1935 256 34 103 90 29
## 1936 483 72 567 94 12
## 1944 512 53 98 81 179
## 1945 1462 16 128 0 0
## 1949 499 149 815 173 49
## 1950 381 35 172 56 86
## 1952 526 80 553 123 94
## 1960 63 151 137 153 19
## 1966 879 143 797 106 143
## 1973 228 19 130 4 11
## 1974 1092 37 592 145 37
## 1976 1276 24 746 94 29
## 1979 448 21 125 52 101
## 1983 90 38 67 33 36
## 1990 575 80 428 208 93
## 2008 753 43 226 69 10
## 2015 315 35 322 46 7
## 2017 384 60 364 119 101
## 2020 555 82 257 93 61
## 2023 848 154 323 201 61
## 2024 202 47 197 55 42
## 2025 433 89 650 16 102
## 2028 167 37 64 49 34
## 2035 367 4 51 6 4
## 2041 532 88 168 69 44
## 2042 724 17 143 0 8
## 2044 587 54 348 71 54
## 2057 180 60 241 13 54
## 2062 758 12 385 33 89
## 2064 196 25 607 67 17
## 2071 471 102 125 212 61
## 2074 240 132 445 250 192
## 2076 1486 55 278 49 0
## 2085 158 35 179 0 0
## 2088 189 10 253 56 43
## 2089 728 17 133 11 8
## 2093 742 28 152 25 9
## 2096 816 42 450 55 56
## 2101 176 72 98 136 21
## 2103 997 15 414 99 30
## 2104 356 0 107 19 9
## 2112 240 67 500 199 0
## 2129 496 32 849 229 48
## 2141 570 73 614 133 102
## 2144 531 69 300 150 138
## 2145 162 28 818 0 28
## 2149 918 10 129 13 0
## 2150 224 155 155 192 93
## 2153 1074 37 518 193 92
## 2154 741 68 689 224 68
## 2163 530 142 217 62 9
## 2166 240 67 500 199 0
## 2171 556 54 845 202 133
## 2172 833 80 363 52 26
## 2179 266 21 300 65 8
## 2181 366 124 156 71 78
## 2189 519 71 860 93 27
## 2195 332 194 377 149 125
## 2197 171 3 129 26 24
## 2199 712 26 538 69 13
## 2203 368 24 68 38 0
## 2204 457 5 106 15 17
## 2205 229 7 137 4 0
## 2215 428 30 214 80 30
## MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases
## 15 45 1 7 6
## 18 176 2 11 4
## 43 174 1 2 4
## 50 241 1 4 9
## 51 109 7 4 2
## 52 190 1 3 10
## 56 80 1 3 6
## 58 169 4 8 2
## 64 109 1 5 2
## 67 86 4 10 3
## 72 51 0 5 8
## 74 51 2 10 1
## 79 76 1 3 10
## 83 145 1 5 6
## 88 75 5 7 4
## 90 223 1 5 7
## 94 111 2 9 5
## 95 81 1 6 3
## 99 65 4 6 4
## 102 79 1 6 6
## 104 60 3 8 1
## 106 72 5 6 4
## 112 48 4 6 2
## 116 168 0 6 8
## 117 172 2 3 4
## 118 216 1 3 6
## 123 130 1 7 2
## 131 50 2 3 5
## 132 126 1 5 7
## 135 49 3 3 3
## 144 76 2 8 5
## 149 57 1 3 4
## 158 124 1 3 4
## 169 223 1 5 7
## 172 141 1 6 4
## 177 117 2 7 1
## 181 102 1 8 7
## 187 51 1 4 6
## 191 92 1 4 4
## 192 54 3 11 6
## 193 58 0 7 9
## 199 192 5 11 4
## 203 44 10 7 1
## 204 54 1 3 4
## 214 92 5 9 6
## 217 92 3 2 2
## 233 181 1 3 9
## 234 152 7 11 5
## 236 80 2 5 3
## 237 77 2 9 4
## 239 70 4 9 6
## 242 148 1 6 9
## 256 241 1 3 7
## 258 116 1 3 3
## 266 162 2 7 4
## 268 54 1 3 5
## 269 131 1 9 3
## 273 66 1 5 1
## 276 74 1 6 3
## 277 135 13 6 2
## 282 203 1 2 7
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## 1927 12 1 0 0 0
## 1929 10 3 0 0 0
## 1930 7 2 0 0 0
## 1935 10 3 0 0 0
## 1936 10 1 0 0 0
## 1944 10 5 0 0 0
## 1945 6 6 0 1 1
## 1949 12 3 0 0 0
## 1950 10 5 0 0 0
## 1952 7 2 0 0 0
## 1960 8 5 0 0 0
## 1966 9 1 0 0 1
## 1973 8 2 0 0 0
## 1974 8 3 0 0 0
## 1976 11 3 0 0 1
## 1979 8 2 1 0 1
## 1983 6 3 0 0 0
## 1990 8 5 0 0 0
## 2008 13 4 0 0 0
## 2015 11 5 0 0 0
## 2017 8 7 0 0 0
## 2020 12 6 0 0 0
## 2023 10 6 1 0 0
## 2024 8 4 0 0 0
## 2025 9 7 0 0 0
## 2028 6 7 0 0 0
## 2035 6 7 0 0 0
## 2041 13 5 0 0 0
## 2042 12 7 0 0 0
## 2044 8 2 0 0 0
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## 2197 6 7 0 0 0
## 2199 11 1 0 1 1
## 2203 6 7 0 0 0
## 2204 6 8 0 0 0
## 2205 8 5 0 0 0
## 2215 10 3 0 0 0
## AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response
## 15 1 0 0 3 11 1
## 18 1 0 0 3 11 0
## 43 0 0 0 3 11 0
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## 1823 0 0 0 3 11 1
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## 1829 1 0 0 3 11 1
## 1834 0 0 1 3 11 1
## 1836 0 0 0 3 11 0
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## 1849 0 0 0 3 11 1
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## 1860 0 0 0 3 11 1
## 1861 1 0 0 3 11 0
## 1862 0 0 0 3 11 0
## 1863 0 0 0 3 11 0
## 1867 0 0 0 3 11 0
## 1875 1 0 0 3 11 0
## 1889 0 0 0 3 11 0
## 1890 0 0 0 3 11 0
## 1898 0 0 0 3 11 0
## 1899 0 1 0 3 11 1
## 1905 0 0 0 3 11 0
## 1912 0 0 0 3 11 1
## 1913 0 0 0 3 11 0
## 1915 0 0 0 3 11 0
## 1918 0 0 0 3 11 0
## 1927 0 0 0 3 11 0
## 1929 0 0 0 3 11 0
## 1930 0 0 0 3 11 1
## 1935 0 0 0 3 11 0
## 1936 0 0 0 3 11 0
## 1944 0 0 0 3 11 0
## 1945 0 1 0 3 11 1
## 1949 0 0 0 3 11 0
## 1950 0 0 0 3 11 0
## 1952 1 0 0 3 11 1
## 1960 0 0 0 3 11 0
## 1966 1 0 0 3 11 0
## 1973 0 0 0 3 11 0
## 1974 1 0 0 3 11 1
## 1976 0 0 0 3 11 1
## 1979 1 0 0 3 11 1
## 1983 0 0 0 3 11 0
## 1990 0 0 0 3 11 0
## 2008 0 0 0 3 11 0
## 2015 0 0 0 3 11 0
## 2017 0 0 0 3 11 0
## 2020 0 0 0 3 11 0
## 2023 0 0 0 3 11 0
## 2024 0 0 0 3 11 0
## 2025 0 0 0 3 11 0
## 2028 0 0 0 3 11 0
## 2035 1 0 0 3 11 0
## 2041 0 0 0 3 11 0
## 2042 0 0 0 3 11 0
## 2044 0 0 0 3 11 0
## 2057 0 0 0 3 11 1
## 2062 0 0 0 3 11 0
## 2064 0 0 0 3 11 0
## 2071 1 0 0 3 11 1
## 2074 0 0 0 3 11 0
## 2076 0 0 0 3 11 1
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## 2093 0 0 0 3 11 0
## 2096 0 0 0 3 11 0
## 2101 0 0 0 3 11 0
## 2103 0 1 0 3 11 0
## 2104 0 0 0 3 11 0
## 2112 0 0 0 3 11 0
## 2129 1 0 0 3 11 0
## 2141 1 0 0 3 11 0
## 2144 0 0 0 3 11 0
## 2145 1 1 0 3 11 1
## 2149 0 1 0 3 11 1
## 2150 0 0 0 3 11 0
## 2153 1 0 0 3 11 1
## 2154 0 0 0 3 11 0
## 2163 0 0 0 3 11 0
## 2166 0 0 0 3 11 0
## 2171 1 0 0 3 11 1
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## 2195 0 0 0 3 11 0
## 2197 0 0 0 3 11 0
## 2199 0 0 0 3 11 1
## 2203 0 0 0 3 11 0
## 2204 0 0 0 3 11 0
## 2205 0 0 0 3 11 0
## 2215 0 0 0 3 11 0
## actual_age Total_Purch test
## 15 75 26 CONSERVATORY
## 18 72 26 CONSERVATORY
## 43 45 16 CONSERVATORY
## 50 44 21 CONSERVATORY
## 51 43 23 CONSERVATORY
## 52 66 27 CONSERVATORY
## 56 39 21 CONSERVATORY
## 58 69 24 CONSERVATORY
## 64 63 18 CONSERVATORY
## 67 62 30 CONSERVATORY
## 72 45 22 CONSERVATORY
## 74 56 21 CONSERVATORY
## 79 34 27 CONSERVATORY
## 83 48 19 CONSERVATORY
## 88 61 22 CONSERVATORY
## 90 70 25 CONSERVATORY
## 94 42 27 CONSERVATORY
## 95 76 16 CONSERVATORY
## 99 52 26 CONSERVATORY
## 102 42 24 CONSERVATORY
## 104 58 20 CONSERVATORY
## 106 41 21 CONSERVATORY
## 112 68 22 CONSERVATORY
## 116 38 27 CONSERVATORY
## 117 56 16 CONSERVATORY
## 118 37 16 CONSERVATORY
## 123 52 19 CONSERVATORY
## 131 45 17 CONSERVATORY
## 132 59 23 CONSERVATORY
## 135 45 15 CONSERVATORY
## 144 69 26 CONSERVATORY
## 149 66 18 CONSERVATORY
## 158 33 15 CONSERVATORY
## 169 70 25 CONSERVATORY
## 172 75 24 CONSERVATORY
## 177 55 18 CONSERVATORY
## 181 32 28 CONSERVATORY
## 187 67 17 CONSERVATORY
## 191 53 18 CONSERVATORY
## 192 43 29 CONSERVATORY
## 193 44 26 CONSERVATORY
## 199 67 29 CONSERVATORY
## 203 47 26 CONSERVATORY
## 204 36 17 CONSERVATORY
## 214 59 32 CONSERVATORY
## 217 69 18 CONSERVATORY
## 233 31 22 CONSERVATORY
## 234 63 31 CONSERVATORY
## 236 49 16 CONSERVATORY
## 237 68 21 CONSERVATORY
## 239 52 25 CONSERVATORY
## 242 47 29 CONSERVATORY
## 256 58 19 CONSERVATORY
## 258 51 13 CONSERVATORY
## 266 65 23 CONSERVATORY
## 268 35 17 CONSERVATORY
## 269 53 22 CONSERVATORY
## 273 47 15 CONSERVATORY
## 276 69 23 CONSERVATORY
## 277 65 33 CONSERVATORY
## 282 41 19 CONSERVATORY
## 295 60 23 CONSERVATORY
## 306 36 15 CONSERVATORY
## 308 50 21 CONSERVATORY
## 323 61 22 CONSERVATORY
## 326 56 22 CONSERVATORY
## 335 51 15 CONSERVATORY
## 339 35 22 CONSERVATORY
## 345 52 15 CONSERVATORY
## 348 64 22 CONSERVATORY
## 352 54 22 CONSERVATORY
## 353 52 19 CONSERVATORY
## 356 59 16 CONSERVATORY
## 361 55 23 CONSERVATORY
## 366 60 20 CONSERVATORY
## 370 67 14 CONSERVATORY
## 375 61 26 CONSERVATORY
## 377 53 19 CONSERVATORY
## 378 42 19 CONSERVATORY
## 380 28 16 CONSERVATORY
## 382 53 23 CONSERVATORY
## 383 30 24 CONSERVATORY
## 402 59 19 CONSERVATORY
## 406 40 26 CONSERVATORY
## 411 47 16 CONSERVATORY
## 415 32 19 CONSERVATORY
## 417 53 24 CONSERVATORY
## 419 32 21 CONSERVATORY
## 420 54 39 CONSERVATORY
## 421 57 25 CONSERVATORY
## 444 55 24 CONSERVATORY
## 448 57 16 CONSERVATORY
## 449 72 22 CONSERVATORY
## 451 58 19 CONSERVATORY
## 457 50 25 CONSERVATORY
## 458 46 33 CONSERVATORY
## 463 65 26 CONSERVATORY
## 466 49 19 CONSERVATORY
## 474 45 17 CONSERVATORY
## 475 47 21 CONSERVATORY
## 478 63 21 CONSERVATORY
## 491 36 21 CONSERVATORY
## 493 68 28 CONSERVATORY
## 495 32 22 CONSERVATORY
## 497 62 25 CONSERVATORY
## 499 42 23 CONSERVATORY
## 501 62 19 CONSERVATORY
## 506 62 17 CONSERVATORY
## 509 34 13 CONSERVATORY
## 510 30 13 CONSERVATORY
## 511 68 16 CONSERVATORY
## 517 61 19 CONSERVATORY
## 518 46 19 CONSERVATORY
## 532 45 20 CONSERVATORY
## 542 72 25 CONSERVATORY
## 544 43 21 CONSERVATORY
## 545 39 19 CONSERVATORY
## 551 51 26 CONSERVATORY
## 580 56 20 CONSERVATORY
## 589 61 23 CONSERVATORY
## 590 66 17 CONSERVATORY
## 591 73 20 CONSERVATORY
## 612 66 21 CONSERVATORY
## 614 46 20 CONSERVATORY
## 619 63 14 CONSERVATORY
## 620 51 26 CONSERVATORY
## 622 48 21 CONSERVATORY
## 624 69 32 CONSERVATORY
## 625 59 14 CONSERVATORY
## 626 71 26 CONSERVATORY
## 627 50 18 CONSERVATORY
## 628 61 26 CONSERVATORY
## 632 74 13 CONSERVATORY
## 634 51 30 CONSERVATORY
## 636 44 28 CONSERVATORY
## 637 63 17 CONSERVATORY
## 646 74 26 CONSERVATORY
## 650 46 24 CONSERVATORY
## 659 59 21 CONSERVATORY
## 660 51 17 CONSERVATORY
## 661 41 20 CONSERVATORY
## 665 42 17 CONSERVATORY
## 669 54 30 CONSERVATORY
## 672 54 17 CONSERVATORY
## 673 45 20 CONSERVATORY
## 690 43 21 CONSERVATORY
## 693 35 22 CONSERVATORY
## 699 56 26 CONSERVATORY
## 701 68 20 CONSERVATORY
## 704 61 17 CONSERVATORY
## 706 51 31 CONSERVATORY
## 709 67 21 CONSERVATORY
## 714 66 24 CONSERVATORY
## 715 45 23 CONSERVATORY
## 717 30 26 CONSERVATORY
## 720 43 23 CONSERVATORY
## 727 52 20 CONSERVATORY
## 733 69 20 CONSERVATORY
## 735 26 24 CONSERVATORY
## 737 67 28 CONSERVATORY
## 740 46 24 CONSERVATORY
## 741 52 25 CONSERVATORY
## 742 42 21 CONSERVATORY
## 743 43 24 CONSERVATORY
## 745 57 20 CONSERVATORY
## 754 44 17 CONSERVATORY
## 755 34 29 CONSERVATORY
## 757 65 18 CONSERVATORY
## 758 42 21 CONSERVATORY
## 759 70 20 CONSERVATORY
## 760 56 25 CONSERVATORY
## 776 43 22 CONSERVATORY
## 785 56 26 CONSERVATORY
## 795 64 17 CONSERVATORY
## 796 49 20 CONSERVATORY
## 797 49 22 CONSERVATORY
## 801 66 27 CONSERVATORY
## 806 49 21 CONSERVATORY
## 819 29 18 CONSERVATORY
## 823 65 28 CONSERVATORY
## 825 41 27 CONSERVATORY
## 832 61 17 CONSERVATORY
## 833 51 19 CONSERVATORY
## 834 30 19 CONSERVATORY
## 835 65 23 CONSERVATORY
## 840 64 25 CONSERVATORY
## 843 42 31 CONSERVATORY
## 845 36 22 CONSERVATORY
## 851 32 17 CONSERVATORY
## 856 43 16 CONSERVATORY
## 862 43 23 CONSERVATORY
## 866 65 28 CONSERVATORY
## 873 51 19 CONSERVATORY
## 877 43 20 CONSERVATORY
## 880 38 25 CONSERVATORY
## 885 63 25 CONSERVATORY
## 888 63 18 CONSERVATORY
## 891 48 13 CONSERVATORY
## 893 44 18 CONSERVATORY
## 900 58 31 CONSERVATORY
## 903 67 14 CONSERVATORY
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## 2215 65 23 CONSERVATORYwhich (cat_num_cols$test == 'TECH')## [1] 1 20 24 30 39 44 65 78 103 109 121 134 142 164 166
## [16] 188 189 205 208 216 238 257 260 271 279 293 311 313 327 333
## [31] 374 391 445 467 470 481 490 502 512 516 519 530 534 558 569
## [46] 571 574 593 631 647 668 670 677 679 680 696 703 711 724 738
## [61] 752 753 756 767 775 778 782 790 805 816 824 827 838 852 864
## [76] 876 879 883 884 917 922 928 931 935 947 990 1010 1023 1031 1036
## [91] 1068 1070 1073 1075 1090 1099 1103 1105 1106 1110 1161 1178 1184 1186 1202
## [106] 1231 1232 1241 1242 1244 1272 1283 1286 1289 1291 1294 1309 1311 1316 1317
## [121] 1318 1339 1370 1374 1383 1387 1398 1400 1431 1438 1449 1457 1484 1491 1499
## [136] 1512 1518 1531 1537 1553 1581 1584 1668 1670 1699 1725 1735 1753 1778 1790
## [151] 1804 1811 1813 1822 1825 1847 1874 1881 1895 1897 1924 1957 1959 1964 1971
## [166] 1980 1991 1994 2018 2032 2047 2056 2060 2061 2075 2077 2086 2091 2111 2117
## [181] 2123 2138 2146 2151 2156 2175 2176 2178 2212cat_num_cols[which (cat_num_cols$test == 'TECH') , ]## Education_2n Cycle Education_Basic Education_Graduation Education_Master
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## 1959 0 1 0
## 1964 0 1 0
## 1971 0 1 0
## 1980 0 1 0
## 1991 1 0 0
## 1994 1 0 0
## 2018 0 1 0
## 2032 0 0 1
## 2047 0 0 0
## 2056 0 1 0
## 2060 0 1 0
## 2061 0 1 0
## 2075 0 0 1
## 2077 0 1 0
## 2086 0 1 0
## 2091 0 0 0
## 2111 0 1 0
## 2117 1 0 0
## 2123 0 0 1
## 2138 0 1 0
## 2146 0 1 0
## 2151 0 1 0
## 2156 0 1 0
## 2175 0 0 0
## 2176 0 1 0
## 2178 0 0 1
## 2212 0 1 0
## Marital_Status_Together Marital_Status_Widow Marital_Status_YOLO
## 1 0 0 0
## 20 0 0 0
## 24 1 0 0
## 30 1 0 0
## 39 0 0 0
## 44 0 0 0
## 65 1 0 0
## 78 1 0 0
## 103 0 0 0
## 109 0 0 0
## 121 0 0 0
## 134 1 0 0
## 142 1 0 0
## 164 0 0 0
## 166 0 0 0
## 188 0 0 0
## 189 0 0 0
## 205 0 0 0
## 208 0 1 0
## 216 1 0 0
## 238 0 0 0
## 257 0 0 0
## 260 0 0 0
## 271 1 0 0
## 279 0 0 0
## 293 0 0 0
## 311 0 0 0
## 313 0 0 0
## 327 1 0 0
## 333 0 0 0
## 374 1 0 0
## 391 1 0 0
## 445 0 0 0
## 467 0 0 0
## 470 0 0 0
## 481 0 0 0
## 490 0 0 0
## 502 1 0 0
## 512 0 0 0
## 516 0 0 0
## 519 0 0 0
## 530 0 0 0
## 534 0 1 0
## 558 0 0 0
## 569 1 0 0
## 571 0 0 0
## 574 0 1 0
## 593 0 0 0
## 631 0 0 0
## 647 0 0 0
## 668 0 0 0
## 670 1 0 0
## 677 1 0 0
## 679 0 0 0
## 680 1 0 0
## 696 1 0 0
## 703 0 0 0
## 711 0 0 0
## 724 0 0 0
## 738 1 0 0
## 752 0 0 0
## 753 0 0 0
## 756 0 0 0
## 767 0 0 0
## 775 0 0 0
## 778 1 0 0
## 782 0 0 0
## 790 0 0 0
## 805 0 0 0
## 816 0 1 0
## 824 0 0 0
## 827 0 0 0
## 838 0 0 0
## 852 0 0 0
## 864 0 0 0
## 876 1 0 0
## 879 0 1 0
## 883 0 0 0
## 884 1 0 0
## 917 0 0 0
## 922 0 0 0
## 928 0 0 0
## 931 1 0 0
## 935 0 0 0
## 947 0 0 0
## 990 0 0 0
## 1010 1 0 0
## 1023 0 0 0
## 1031 1 0 0
## 1036 1 0 0
## 1068 0 0 0
## 1070 0 0 0
## 1073 0 0 0
## 1075 1 0 0
## 1090 0 0 0
## 1099 0 0 0
## 1103 1 0 0
## 1105 0 0 0
## 1106 1 0 0
## 1110 0 0 0
## 1161 0 0 0
## 1178 0 0 0
## 1184 0 0 0
## 1186 0 0 0
## 1202 0 0 0
## 1231 1 0 0
## 1232 1 0 0
## 1241 0 0 0
## 1242 0 0 0
## 1244 0 0 0
## 1272 0 0 0
## 1283 1 0 0
## 1286 0 0 0
## 1289 1 0 0
## 1291 0 0 0
## 1294 1 0 0
## 1309 1 0 0
## 1311 0 0 0
## 1316 0 0 0
## 1317 1 0 0
## 1318 0 0 0
## 1339 0 0 0
## 1370 0 0 0
## 1374 0 0 0
## 1383 0 0 0
## 1387 0 0 0
## 1398 0 0 0
## 1400 1 0 0
## 1431 0 0 0
## 1438 1 0 0
## 1449 0 0 0
## 1457 0 0 0
## 1484 1 0 0
## 1491 1 0 0
## 1499 0 0 0
## 1512 0 0 0
## 1518 0 0 0
## 1531 0 0 0
## 1537 1 0 0
## 1553 0 0 0
## 1581 0 0 0
## 1584 0 0 0
## 1668 0 0 0
## 1670 0 0 0
## 1699 1 0 0
## 1725 1 0 0
## 1735 0 0 0
## 1753 0 0 0
## 1778 1 0 0
## 1790 0 0 0
## 1804 1 0 0
## 1811 1 0 0
## 1813 1 0 0
## 1822 0 0 0
## 1825 0 1 0
## 1847 0 0 0
## 1874 0 0 0
## 1881 0 0 0
## 1895 0 0 0
## 1897 0 0 0
## 1924 0 0 0
## 1957 0 0 0
## 1959 0 0 0
## 1964 0 0 0
## 1971 0 0 0
## 1980 0 0 0
## 1991 0 0 0
## 1994 0 0 0
## 2018 0 0 0
## 2032 0 0 0
## 2047 0 1 0
## 2056 0 0 0
## 2060 0 0 0
## 2061 0 0 0
## 2075 0 0 0
## 2077 0 0 0
## 2086 0 0 0
## 2091 1 0 0
## 2111 0 0 0
## 2117 0 0 0
## 2123 0 0 0
## 2138 0 0 0
## 2146 0 0 0
## 2151 0 0 0
## 2156 0 0 0
## 2175 1 0 0
## 2176 0 0 0
## 2178 0 0 0
## 2212 0 0 0
## Country_AUS Country_CA Country_GER Country_IND Country_ME Country_SA
## 1 0 0 0 0 0 0
## 20 0 0 0 0 0 0
## 24 0 0 0 0 0 0
## 30 1 0 0 0 0 0
## 39 1 0 0 0 0 0
## 44 1 0 0 0 0 0
## 65 0 0 1 0 0 0
## 78 0 0 1 0 0 0
## 103 0 1 0 0 0 0
## 109 0 1 0 0 0 0
## 121 0 1 0 0 0 0
## 134 0 1 0 0 0 0
## 142 0 1 0 0 0 0
## 164 0 0 0 1 0 0
## 166 0 0 0 1 0 0
## 188 0 0 0 1 0 0
## 189 0 0 0 1 0 0
## 205 0 0 0 0 0 1
## 208 0 0 0 0 0 1
## 216 0 0 0 0 0 1
## 238 0 0 0 0 0 1
## 257 0 0 0 0 0 1
## 260 0 0 0 0 0 1
## 271 0 0 0 0 0 1
## 279 0 0 0 0 0 1
## 293 0 0 0 0 0 1
## 311 0 0 0 0 0 0
## 313 0 0 0 0 0 0
## 327 0 0 0 0 0 0
## 333 0 0 0 0 0 0
## 374 0 0 0 0 0 0
## 391 0 0 0 0 0 0
## 445 0 0 0 0 0 0
## 467 0 0 0 0 0 0
## 470 0 0 0 0 0 0
## 481 0 0 0 0 0 0
## 490 0 0 0 0 0 0
## 502 0 0 0 0 0 0
## 512 0 0 0 0 0 0
## 516 0 0 0 0 0 0
## 519 0 0 0 0 0 0
## 530 0 0 0 0 0 0
## 534 0 0 0 0 0 0
## 558 0 0 0 0 0 0
## 569 0 0 0 0 0 0
## 571 0 0 0 0 0 0
## 574 0 0 0 0 0 0
## 593 0 0 0 0 0 0
## 631 0 0 0 0 0 0
## 647 0 0 0 0 0 0
## 668 0 0 0 0 0 0
## 670 0 0 0 0 0 0
## 677 0 0 0 0 0 0
## 679 0 0 0 0 0 0
## 680 0 0 0 0 0 0
## 696 1 0 0 0 0 0
## 703 1 0 0 0 0 0
## 711 1 0 0 0 0 0
## 724 1 0 0 0 0 0
## 738 0 0 1 0 0 0
## 752 0 0 1 0 0 0
## 753 0 0 1 0 0 0
## 756 0 0 1 0 0 0
## 767 0 1 0 0 0 0
## 775 0 1 0 0 0 0
## 778 0 1 0 0 0 0
## 782 0 1 0 0 0 0
## 790 0 1 0 0 0 0
## 805 0 1 0 0 0 0
## 816 0 1 0 0 0 0
## 824 0 1 0 0 0 0
## 827 0 1 0 0 0 0
## 838 0 0 0 1 0 0
## 852 0 0 0 1 0 0
## 864 0 0 0 1 0 0
## 876 0 0 0 0 0 1
## 879 0 0 0 0 0 1
## 883 0 0 0 0 0 1
## 884 0 0 0 0 0 1
## 917 0 0 0 0 0 1
## 922 0 0 0 0 0 1
## 928 0 0 0 0 0 1
## 931 0 0 0 0 0 1
## 935 0 0 0 0 0 1
## 947 0 0 0 0 0 1
## 990 0 0 0 0 0 0
## 1010 0 0 0 0 0 0
## 1023 0 0 0 0 0 0
## 1031 0 0 0 0 0 0
## 1036 0 0 0 0 0 0
## 1068 0 0 0 0 0 0
## 1070 0 0 0 0 0 0
## 1073 0 0 0 0 0 0
## 1075 0 0 0 0 0 0
## 1090 0 0 0 0 0 0
## 1099 0 0 0 0 0 0
## 1103 0 0 0 0 0 0
## 1105 0 0 0 0 0 0
## 1106 0 0 0 0 0 0
## 1110 0 0 0 0 0 0
## 1161 0 0 0 0 0 0
## 1178 0 0 0 0 0 0
## 1184 0 0 0 0 0 0
## 1186 0 0 0 0 0 0
## 1202 0 0 0 0 0 0
## 1231 0 0 0 0 0 0
## 1232 0 0 0 0 0 0
## 1241 0 0 0 0 0 0
## 1242 0 0 0 0 0 0
## 1244 0 0 0 0 0 0
## 1272 0 0 0 0 0 0
## 1283 0 0 0 0 0 0
## 1286 0 0 0 0 0 0
## 1289 0 0 0 0 0 0
## 1291 0 0 0 0 0 0
## 1294 0 0 0 0 0 0
## 1309 0 0 0 0 0 0
## 1311 0 0 0 0 0 0
## 1316 0 0 0 0 0 0
## 1317 0 0 0 0 0 0
## 1318 0 0 0 0 0 0
## 1339 0 0 0 0 1 0
## 1370 1 0 0 0 0 0
## 1374 1 0 0 0 0 0
## 1383 1 0 0 0 0 0
## 1387 1 0 0 0 0 0
## 1398 1 0 0 0 0 0
## 1400 1 0 0 0 0 0
## 1431 0 0 1 0 0 0
## 1438 0 1 0 0 0 0
## 1449 0 1 0 0 0 0
## 1457 0 1 0 0 0 0
## 1484 0 1 0 0 0 0
## 1491 0 1 0 0 0 0
## 1499 0 1 0 0 0 0
## 1512 0 0 0 1 0 0
## 1518 0 0 0 1 0 0
## 1531 0 0 0 1 0 0
## 1537 0 0 0 1 0 0
## 1553 0 0 0 0 0 1
## 1581 0 0 0 0 0 1
## 1584 0 0 0 0 0 1
## 1668 0 0 0 0 0 0
## 1670 0 0 0 0 0 0
## 1699 0 0 0 0 0 0
## 1725 0 0 0 0 0 0
## 1735 0 0 0 0 0 0
## 1753 0 0 0 0 0 0
## 1778 0 0 0 0 0 0
## 1790 0 0 0 0 0 0
## 1804 0 0 0 0 0 0
## 1811 0 0 0 0 0 0
## 1813 0 0 0 0 0 0
## 1822 0 0 0 0 0 0
## 1825 0 0 0 0 0 0
## 1847 0 0 0 0 0 0
## 1874 0 0 0 0 0 0
## 1881 0 0 0 0 0 0
## 1895 0 0 0 0 0 0
## 1897 0 0 0 0 0 0
## 1924 0 0 0 0 0 0
## 1957 0 0 0 0 0 0
## 1959 0 0 0 0 0 0
## 1964 0 0 0 0 0 0
## 1971 0 0 0 0 0 0
## 1980 0 0 0 0 0 0
## 1991 0 0 0 0 0 0
## 1994 0 0 0 0 0 0
## 2018 0 0 0 0 0 0
## 2032 0 0 0 0 0 0
## 2047 1 0 0 0 0 0
## 2056 1 0 0 0 0 0
## 2060 1 0 0 0 0 0
## 2061 1 0 0 0 0 0
## 2075 1 0 0 0 0 0
## 2077 0 0 1 0 0 0
## 2086 0 0 1 0 0 0
## 2091 0 0 1 0 0 0
## 2111 0 1 0 0 0 0
## 2117 0 1 0 0 0 0
## 2123 0 1 0 0 0 0
## 2138 0 1 0 0 0 0
## 2146 0 1 0 0 0 0
## 2151 0 1 0 0 0 0
## 2156 0 1 0 0 0 0
## 2175 0 0 0 1 0 0
## 2176 0 0 0 1 0 0
## 2178 0 0 0 1 0 0
## 2212 0 0 0 0 0 1
## Country_SP Country_US ID Year_Birth Income Kidhome Teenhome Recency
## 1 0 1 5524 1957 58138 0 0 58
## 20 0 1 9360 1982 37040 0 0 41
## 24 0 1 1409 1951 40689 0 1 69
## 30 0 0 3033 1963 38620 0 0 56
## 39 0 0 8601 1980 80011 0 1 3
## 44 0 0 9909 1996 7500 0 0 24
## 65 0 0 1357 1970 74854 1 2 90
## 78 0 0 4119 1963 38620 0 0 56
## 103 0 0 7431 1991 68126 0 0 40
## 109 0 0 367 1978 36550 1 0 74
## 121 0 0 8026 1952 62998 0 1 10
## 134 0 0 9701 1988 41883 1 0 13
## 142 0 0 7798 1972 46344 0 1 28
## 164 0 0 641 1967 52074 0 1 77
## 166 0 0 10314 1948 83837 0 0 79
## 188 0 0 6422 1954 86718 0 0 20
## 189 0 0 3182 1973 46854 1 1 81
## 205 0 0 1584 1977 41443 1 0 79
## 208 0 0 1377 1954 44551 0 1 24
## 216 0 0 7375 1976 51369 0 1 84
## 238 0 0 8867 1988 67546 0 0 90
## 257 0 0 9703 1970 38097 1 1 70
## 260 0 0 2379 1953 69267 0 0 31
## 271 0 0 10144 1976 51369 0 1 84
## 279 0 0 4697 1949 35416 0 0 62
## 293 0 0 1717 1988 39747 1 0 43
## 311 1 0 9750 1961 64325 0 1 41
## 313 1 0 7214 1957 62187 0 0 49
## 327 1 0 1150 1899 83532 0 0 36
## 333 1 0 1411 1952 82623 0 0 58
## 374 1 0 6720 1968 43795 0 1 11
## 391 1 0 615 1960 30507 0 0 29
## 445 1 0 3225 1984 42207 1 0 71
## 467 1 0 10581 1977 41124 1 0 41
## 470 1 0 8346 1974 32233 1 0 26
## 481 1 0 9426 1964 58308 0 1 77
## 490 1 0 8151 1990 24279 0 0 6
## 502 1 0 182 1946 44124 0 1 62
## 512 1 0 35 1987 25545 1 0 35
## 516 1 0 4702 1964 61839 0 0 20
## 519 1 0 2004 1969 72679 0 1 65
## 530 1 0 5036 1984 42710 1 0 38
## 534 1 0 10955 1964 85620 0 0 68
## 558 1 0 6818 1974 44989 0 1 26
## 569 1 0 5314 1951 40689 0 1 69
## 571 1 0 5123 1951 27450 0 0 57
## 574 1 0 9058 1955 79800 0 0 65
## 593 1 0 7141 1986 21888 1 0 15
## 631 1 0 2942 1964 45906 0 1 20
## 647 1 0 6940 1966 46734 1 2 86
## 668 0 1 2891 1963 68118 0 1 51
## 670 0 1 75 1982 41039 0 0 32
## 677 0 1 7279 1969 69476 0 0 3
## 679 0 1 143 1970 61209 0 0 73
## 680 0 1 450 1958 42315 0 1 90
## 696 0 0 3336 1948 66375 0 1 96
## 703 0 0 10479 1975 76618 0 0 64
## 711 0 0 10936 1965 72190 0 0 79
## 724 0 0 7072 1973 67432 0 1 69
## 738 0 0 6974 1972 83443 0 0 89
## 752 0 0 4508 1952 75127 0 0 92
## 753 0 0 221 1955 48726 0 1 90
## 756 0 0 20 1965 46891 0 1 91
## 767 0 0 10736 1971 72258 0 1 28
## 775 0 0 6299 1968 42564 0 1 28
## 778 0 0 6543 1945 62847 0 0 45
## 782 0 0 4442 1961 46524 0 1 70
## 790 0 0 236 1951 34838 1 1 62
## 805 0 0 7462 1954 22507 0 0 67
## 816 0 0 2150 1958 80995 0 1 83
## 824 0 0 6141 1972 49494 1 0 46
## 827 0 0 5121 1977 56628 0 1 30
## 838 0 0 7848 1974 49166 0 1 17
## 852 0 0 4120 1975 49514 1 0 61
## 864 0 0 773 1971 42835 1 1 64
## 876 0 0 8825 1962 51195 1 1 46
## 879 0 0 7108 1965 51390 1 1 54
## 883 0 0 9224 1972 55260 0 1 81
## 884 0 0 5519 1956 64090 0 1 8
## 917 0 0 10673 1976 68397 0 1 6
## 922 0 0 4843 1952 75127 0 0 92
## 928 0 0 8957 1952 64831 1 1 51
## 931 0 0 5386 1953 94384 0 0 62
## 935 0 0 8405 1979 68274 1 1 83
## 947 0 0 9665 1957 54237 0 1 48
## 990 1 0 9984 1981 56337 1 1 25
## 1010 1 0 6059 1953 64504 1 2 81
## 1023 1 0 9503 1980 28071 0 0 65
## 1031 1 0 4998 1962 76081 0 0 85
## 1036 1 0 8870 1984 35196 1 0 68
## 1068 1 0 3535 1965 66294 0 0 68
## 1070 1 0 8707 1956 79456 0 0 12
## 1073 1 0 6295 1977 54233 0 1 46
## 1075 1 0 1509 1958 84460 0 0 80
## 1090 1 0 1545 1986 38508 1 0 95
## 1099 1 0 1524 1983 81698 0 0 1
## 1103 1 0 4356 1971 71819 0 1 70
## 1105 1 0 1175 1983 36262 0 0 24
## 1106 1 0 880 1966 22634 0 0 47
## 1110 1 0 675 1973 52034 1 1 67
## 1161 1 0 10660 1957 66726 1 1 61
## 1178 1 0 590 1970 78579 0 0 35
## 1184 1 0 2246 1965 46891 0 1 91
## 1186 1 0 5545 1972 78075 0 0 72
## 1202 1 0 3439 1972 56721 1 1 64
## 1231 1 0 7718 1947 66000 0 0 36
## 1232 1 0 1291 1978 27683 1 0 90
## 1241 1 0 961 1972 74716 0 1 92
## 1242 1 0 5011 1963 68118 0 1 51
## 1244 1 0 9094 1955 62972 0 1 39
## 1272 1 0 5043 1951 26997 0 0 89
## 1283 1 0 3551 1954 60033 0 1 28
## 1286 1 0 1577 1946 78569 0 0 14
## 1289 1 0 6024 1953 94384 0 0 62
## 1291 1 0 3099 1970 44267 1 1 48
## 1294 1 0 3972 1976 50200 1 1 70
## 1309 1 0 10377 1958 46692 0 1 37
## 1311 1 0 10971 1977 37774 2 0 28
## 1316 1 0 4246 1982 6560 0 0 2
## 1317 1 0 158 1945 71604 0 0 3
## 1318 1 0 4832 1979 27244 1 0 84
## 1339 0 0 5080 1993 70515 0 0 12
## 1370 0 0 10909 1948 92344 0 0 9
## 1374 0 0 8216 1972 53761 1 1 7
## 1383 0 0 10490 1969 51039 1 1 11
## 1387 0 0 4188 1957 36864 0 1 53
## 1398 0 0 2678 1990 34412 1 0 62
## 1400 0 0 524 1966 22634 0 0 47
## 1431 0 0 5221 1957 52852 0 1 93
## 1438 0 0 6875 1965 32727 0 0 38
## 1449 0 0 10524 1963 49476 0 1 29
## 1457 0 0 4070 1969 94871 0 2 99
## 1484 0 0 4093 1975 53253 1 1 61
## 1491 0 0 7706 1975 46772 0 1 88
## 1499 0 0 2853 1980 51766 1 0 74
## 1512 0 0 830 1987 74004 0 0 5
## 1518 0 0 3745 1988 23331 1 0 92
## 1531 0 0 5636 1963 30983 0 0 50
## 1537 0 0 7321 1962 76081 0 0 85
## 1553 0 0 1081 1957 75283 1 2 26
## 1581 0 0 3409 1984 36108 1 0 68
## 1584 0 0 5272 1965 53843 0 1 64
## 1668 1 0 10323 1952 49413 0 1 88
## 1670 1 0 1891 1980 78789 0 0 12
## 1699 1 0 10340 1984 35196 1 0 68
## 1725 1 0 7610 1983 37292 1 0 32
## 1735 1 0 7807 1974 44989 0 1 26
## 1753 1 0 5534 1975 47808 0 1 30
## 1778 1 0 9905 1952 34074 1 1 69
## 1790 1 0 6237 1966 7144 0 2 92
## 1804 1 0 2415 1962 62568 0 1 99
## 1811 1 0 5302 1986 78394 0 0 13
## 1813 1 0 1087 1975 22669 1 0 30
## 1822 1 0 9847 1955 62972 0 1 39
## 1825 1 0 6906 1953 84953 0 0 73
## 1847 1 0 3254 1971 70886 1 0 65
## 1874 1 0 2747 1988 67546 0 0 90
## 1881 1 0 5558 1954 90933 0 0 90
## 1895 1 0 5544 1970 67384 0 1 32
## 1897 1 0 5831 1967 77870 0 1 93
## 1924 1 0 1139 1984 73356 0 0 56
## 1957 1 0 9507 1975 40794 0 1 18
## 1959 1 0 10311 1969 4428 0 1 0
## 1964 1 0 3032 1984 24570 1 0 97
## 1971 1 0 4552 1991 51373 0 0 37
## 1980 1 0 4122 1975 79205 0 0 73
## 1991 1 0 405 1964 41638 0 1 68
## 1994 1 0 2002 1958 71964 0 0 81
## 2018 0 1 983 1984 40059 1 0 82
## 2032 0 1 3507 1970 76467 1 0 44
## 2047 0 0 123 1951 67046 0 1 92
## 2056 0 0 2254 1983 69520 0 0 95
## 2060 0 0 4643 1973 27803 1 0 40
## 2061 0 0 6497 1960 51651 0 1 32
## 2075 0 0 975 1956 54252 1 1 25
## 2077 0 0 3422 1975 76068 0 1 67
## 2086 0 0 5991 1949 48150 0 1 24
## 2091 0 0 3969 1974 43018 0 1 46
## 2111 0 0 642 1980 16005 1 0 69
## 2117 0 0 2715 1966 29435 1 1 11
## 2123 0 0 9790 1957 78499 0 0 12
## 2138 0 0 5667 1964 60896 0 1 90
## 2146 0 0 10394 1984 90000 0 0 91
## 2151 0 0 6721 1951 71965 0 1 21
## 2156 0 0 4377 1971 52914 0 1 32
## 2175 0 0 10722 1973 55593 0 1 63
## 2176 0 0 2632 1954 50501 1 1 10
## 2178 0 0 7620 1990 16185 1 0 71
## 2212 0 0 10870 1967 61223 0 1 46
## MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts
## 1 635 88 546 172 88
## 20 86 2 73 69 38
## 24 270 3 27 39 6
## 30 112 17 44 34 22
## 39 421 76 536 82 178
## 44 3 18 14 15 22
## 65 856 59 487 58 29
## 78 112 17 44 34 22
## 103 1332 17 311 23 51
## 109 47 90 94 123 103
## 121 120 58 73 65 89
## 134 32 34 41 73 16
## 142 233 20 57 8 20
## 164 59 0 12 0 0
## 166 847 66 119 86 89
## 188 344 189 482 50 33
## 189 96 1 79 7 1
## 205 171 7 171 25 19
## 208 182 4 33 0 0
## 216 297 7 79 0 11
## 238 864 134 768 150 38
## 257 77 2 35 0 1
## 260 778 44 499 95 73
## 271 297 7 79 0 11
## 279 248 3 81 4 3
## 293 80 15 93 20 13
## 311 731 60 353 78 12
## 313 792 0 275 45 45
## 327 755 144 562 104 64
## 333 204 34 204 172 153
## 374 314 11 53 4 3
## 391 65 36 74 38 20
## 445 116 11 72 3 17
## 467 281 7 84 15 0
## 470 138 15 35 25 11
## 481 691 0 69 10 0
## 490 16 36 21 20 62
## 502 284 0 55 0 6
## 512 32 1 64 16 12
## 516 1000 155 379 224 17
## 519 619 54 260 127 54
## 530 252 3 42 4 3
## 534 416 46 925 60 107
## 558 98 0 106 49 10
## 569 270 3 27 39 6
## 571 37 12 23 8 11
## 574 1060 21 530 32 0
## 593 88 10 46 2 3
## 631 305 3 27 4 3
## 647 100 1 39 6 1
## 668 595 23 123 10 23
## 670 34 11 137 179 61
## 677 260 86 559 63 9
## 679 466 0 224 119 49
## 680 67 15 80 17 11
## 696 712 0 45 0 0
## 703 749 40 294 121 160
## 711 597 166 597 172 166
## 724 432 79 341 177 136
## 738 518 42 742 55 56
## 752 203 35 305 46 17
## 753 138 3 38 4 0
## 756 43 12 23 29 15
## 767 522 0 522 227 120
## 775 324 48 186 39 18
## 778 521 168 706 80 76
## 782 31 19 35 26 14
## 790 28 23 29 29 14
## 805 68 0 226 51 157
## 816 860 28 409 73 56
## 824 191 9 97 0 6
## 827 479 5 82 7 17
## 838 224 2 25 7 0
## 852 88 39 78 58 13
## 864 379 4 93 12 9
## 876 230 14 156 82 24
## 879 205 20 47 23 2
## 883 825 8 53 11 0
## 884 316 58 161 76 51
## 917 760 80 466 17 13
## 922 203 35 305 46 17
## 928 480 86 249 75 86
## 931 1111 24 790 160 45
## 935 135 25 51 23 25
## 947 267 3 30 4 0
## 990 349 16 144 28 5
## 1010 986 36 168 16 0
## 1023 39 33 130 41 26
## 1031 292 30 415 63 33
## 1036 75 12 141 43 39
## 1068 251 108 334 65 92
## 1070 565 42 548 64 83
## 1073 652 8 158 21 0
## 1075 152 27 103 106 54
## 1090 120 19 206 25 19
## 1099 709 45 115 30 160
## 1103 1224 28 140 0 14
## 1105 88 39 54 64 28
## 1106 2 23 11 8 6
## 1110 146 0 93 7 13
## 1161 349 7 35 0 0
## 1178 816 66 549 216 66
## 1184 43 12 23 29 15
## 1186 572 8 259 34 35
## 1202 157 6 39 6 13
## 1231 244 51 270 101 0
## 1232 152 9 121 12 12
## 1241 133 27 421 13 195
## 1242 595 23 123 10 23
## 1244 313 15 47 20 0
## 1272 7 23 78 133 49
## 1283 62 1 57 19 11
## 1286 558 79 622 207 175
## 1289 1111 24 790 160 45
## 1291 183 5 65 3 5
## 1294 266 6 51 8 6
## 1309 170 6 97 24 12
## 1311 173 8 107 7 2
## 1316 67 11 26 4 3
## 1317 345 53 528 98 75
## 1318 6 5 17 3 24
## 1339 420 0 452 182 64
## 1370 992 24 694 51 19
## 1374 158 0 76 13 7
## 1383 100 64 79 65 0
## 1387 204 5 39 17 0
## 1398 52 12 50 4 36
## 1400 2 23 11 8 6
## 1431 714 8 99 11 0
## 1438 167 13 180 86 13
## 1449 386 23 95 54 41
## 1457 169 24 553 188 0
## 1484 216 9 57 20 9
## 1491 350 104 189 197 151
## 1499 60 51 87 6 20
## 1512 784 48 560 42 176
## 1518 5 36 7 0 10
## 1531 51 4 50 12 1
## 1537 292 30 415 63 33
## 1553 733 9 180 12 19
## 1581 141 8 129 3 11
## 1584 378 0 88 19 4
## 1668 205 7 41 3 2
## 1670 667 50 850 21 83
## 1699 75 12 141 43 39
## 1725 275 0 86 10 23
## 1735 98 0 106 49 10
## 1753 123 1 26 2 0
## 1778 135 1 41 10 1
## 1790 81 4 33 5 2
## 1804 362 17 398 80 35
## 1811 298 27 697 216 24
## 1813 16 14 36 37 22
## 1822 313 15 47 20 0
## 1825 167 48 602 63 72
## 1847 407 70 239 103 88
## 1874 864 134 768 150 38
## 1881 1020 31 430 62 63
## 1895 957 40 175 158 53
## 1897 1017 50 500 65 50
## 1924 1050 14 322 0 14
## 1957 261 23 73 4 23
## 1959 16 4 12 2 4
## 1964 28 34 62 11 14
## 1971 83 2 101 64 26
## 1980 504 23 117 179 28
## 1991 315 0 31 4 0
## 1994 587 51 932 180 34
## 2018 110 29 92 28 10
## 2032 676 161 426 210 44
## 2047 544 35 133 23 151
## 2056 627 120 414 121 80
## 2060 8 26 46 38 9
## 2061 293 6 23 13 6
## 2075 178 4 26 8 4
## 2077 1115 12 128 33 12
## 2086 173 2 39 3 2
## 2091 35 4 17 7 6
## 2111 1 3 2 20 30
## 2117 70 3 37 4 2
## 2123 912 72 170 47 36
## 2138 1013 30 399 60 46
## 2146 675 144 133 94 192
## 2151 572 19 286 50 38
## 2156 254 10 44 30 10
## 2175 293 8 124 11 4
## 2176 297 0 38 13 0
## 2178 5 11 16 21 8
## 2212 709 43 182 42 118
## MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases
## 1 88 3 8 10
## 20 48 1 4 2
## 24 99 7 7 1
## 30 89 1 2 5
## 39 102 2 8 6
## 44 50 3 3 1
## 65 73 7 2 5
## 78 89 1 2 5
## 103 86 1 7 4
## 109 120 5 9 1
## 121 58 5 5 4
## 134 116 3 4 2
## 142 91 4 7 1
## 164 48 1 2 2
## 166 111 1 9 10
## 188 172 1 5 6
## 189 79 6 5 1
## 205 89 6 8 1
## 208 66 2 4 1
## 216 182 2 8 2
## 238 172 1 4 10
## 257 69 4 3 2
## 260 146 2 3 4
## 271 182 2 8 2
## 279 51 3 7 1
## 293 52 3 5 1
## 311 73 5 10 3
## 313 126 1 4 8
## 327 224 1 4 6
## 333 173 1 2 9
## 374 107 3 7 3
## 391 110 1 5 1
## 445 46 4 4 1
## 467 119 7 7 3
## 470 72 2 6 2
## 481 130 4 2 6
## 490 108 1 4 1
## 502 52 4 6 2
## 512 85 3 2 2
## 516 120 1 4 8
## 519 54 1 4 7
## 530 102 2 4 5
## 534 46 1 3 6
## 558 106 5 5 1
## 569 99 7 7 1
## 571 52 1 2 1
## 574 224 1 5 11
## 593 64 4 5 1
## 631 144 2 5 4
## 647 76 4 3 2
## 668 154 2 8 9
## 670 45 1 8 1
## 677 67 1 4 6
## 679 99 1 5 3
## 680 80 3 1 4
## 696 191 3 2 4
## 703 147 1 3 2
## 711 249 1 5 6
## 724 170 2 8 5
## 738 84 1 6 10
## 752 227 1 2 11
## 753 59 3 3 2
## 756 61 1 2 1
## 767 134 2 6 9
## 775 198 6 6 8
## 778 61 1 3 6
## 782 52 2 1 1
## 790 47 3 2 1
## 805 68 3 10 2
## 816 56 1 8 9
## 824 82 2 6 1
## 827 171 2 7 6
## 838 110 2 5 3
## 852 93 2 6 1
## 864 98 7 6 6
## 876 58 8 9 2
## 879 56 6 5 2
## 883 242 2 8 10
## 884 135 1 6 9
## 917 80 1 6 9
## 922 227 1 2 11
## 928 144 8 6 3
## 931 172 0 5 8
## 935 46 2 4 2
## 947 57 4 5 2
## 990 182 5 8 5
## 1010 108 7 11 3
## 1023 44 2 5 1
## 1031 200 1 4 5
## 1036 187 6 6 1
## 1068 117 1 4 3
## 1070 99 1 5 6
## 1073 142 4 11 6
## 1075 81 0 9 9
## 1090 54 6 7 2
## 1099 45 1 8 2
## 1103 168 3 3 3
## 1105 153 1 3 5
## 1106 46 1 2 1
## 1110 73 5 4 2
## 1161 47 3 8 2
## 1178 99 1 5 6
## 1184 61 1 2 1
## 1186 80 1 3 5
## 1202 75 5 4 1
## 1231 45 1 1 3
## 1232 45 4 6 2
## 1241 71 2 7 3
## 1242 154 2 8 9
## 1244 192 2 7 4
## 1272 144 2 4 2
## 1283 48 2 2 1
## 1286 95 1 4 6
## 1289 172 0 5 8
## 1291 49 5 5 2
## 1294 64 6 7 1
## 1309 88 3 6 1
## 1311 176 4 7 2
## 1316 262 0 1 0
## 1317 97 1 8 3
## 1318 46 2 2 2
## 1339 140 1 6 6
## 1370 119 0 5 10
## 1374 71 6 4 4
## 1383 109 2 6 2
## 1387 89 3 5 2
## 1398 55 3 5 0
## 1400 46 1 2 1
## 1431 47 4 10 7
## 1438 70 2 7 3
## 1449 196 4 2 11
## 1457 144 1 8 5
## 1484 125 7 4 3
## 1491 57 6 11 8
## 1499 51 2 4 2
## 1512 48 1 4 6
## 1518 45 2 3 0
## 1531 49 1 4 0
## 1537 200 1 4 5
## 1553 66 11 6 3
## 1581 47 3 7 1
## 1584 73 4 8 5
## 1668 46 2 4 2
## 1670 83 1 4 6
## 1699 187 6 6 1
## 1725 66 5 6 3
## 1735 106 5 5 1
## 1753 72 2 3 2
## 1778 67 5 3 3
## 1790 291 0 23 1
## 1804 61 3 5 3
## 1811 166 1 4 6
## 1813 48 3 4 1
## 1822 192 2 7 4
## 1825 72 1 3 10
## 1847 53 1 5 4
## 1874 172 1 4 10
## 1881 79 1 4 6
## 1895 107 2 7 8
## 1897 133 3 5 5
## 1924 112 1 5 11
## 1957 50 2 6 3
## 1959 321 0 25 0
## 1964 44 3 4 0
## 1971 117 1 3 5
## 1980 44 1 6 5
## 1991 91 4 5 5
## 1994 86 1 5 6
## 2018 145 1 6 2
## 2032 58 2 2 5
## 2047 107 2 4 5
## 2056 93 1 9 6
## 2060 49 2 3 0
## 2061 92 4 6 2
## 2075 44 5 4 1
## 2077 76 1 2 3
## 2086 47 3 5 1
## 2091 64 1 2 2
## 2111 47 3 2 1
## 2117 64 4 3 2
## 2123 97 1 11 3
## 2138 153 3 9 10
## 2146 241 1 4 8
## 2151 248 3 6 3
## 2156 227 2 7 3
## 2175 106 2 6 6
## 2176 152 7 5 4
## 2178 45 2 2 2
## 2212 247 2 9 3
## NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5
## 1 4 7 0 0 0
## 20 5 8 0 0 0
## 24 5 8 0 0 0
## 30 3 3 0 0 0
## 39 5 4 0 0 0
## 44 3 9 0 0 0
## 65 5 5 0 0 0
## 78 3 3 0 0 0
## 103 5 9 0 1 0
## 109 5 9 0 0 0
## 121 5 5 0 0 0
## 134 3 7 0 0 0
## 142 5 7 0 0 0
## 164 2 4 1 0 0
## 166 5 4 0 1 0
## 188 5 2 0 0 0
## 189 3 8 1 0 0
## 205 5 8 0 0 0
## 208 5 7 0 0 0
## 216 4 8 0 0 0
## 238 5 3 0 0 0
## 257 2 7 1 0 0
## 260 5 7 0 0 0
## 271 4 8 0 0 0
## 279 5 8 0 0 0
## 293 4 8 0 0 0
## 311 5 7 0 0 0
## 313 5 3 0 0 0
## 327 4 1 0 0 1
## 333 4 1 0 0 0
## 374 4 7 0 1 0
## 391 4 7 0 0 0
## 445 5 6 1 0 0
## 467 4 8 0 0 0
## 470 2 9 1 0 0
## 481 3 8 0 0 0
## 490 3 8 0 0 0
## 502 5 8 0 0 0
## 512 3 6 0 0 0
## 516 4 8 0 0 0
## 519 5 1 0 0 0
## 530 3 6 0 0 0
## 534 4 1 0 0 0
## 558 5 6 0 0 0
## 569 5 8 0 0 0
## 571 3 7 0 0 0
## 574 5 3 1 0 1
## 593 2 10 0 0 0
## 631 4 5 1 0 0
## 647 3 6 1 0 0
## 668 4 6 0 0 0
## 670 5 8 0 0 0
## 677 4 2 0 0 0
## 679 4 2 0 0 0
## 680 4 3 0 0 0
## 696 5 7 1 0 0
## 703 5 1 0 0 0
## 711 4 3 0 0 0
## 724 4 4 0 0 0
## 738 5 2 0 0 0
## 752 5 1 0 0 0
## 753 4 6 0 0 0
## 756 4 4 0 0 0
## 767 5 2 0 0 0
## 775 4 7 1 0 0
## 778 4 2 0 0 0
## 782 5 3 0 0 0
## 790 4 6 0 0 0
## 805 4 9 0 0 0
## 816 4 4 0 0 0
## 824 5 7 0 0 0
## 827 5 5 1 0 0
## 838 3 6 0 0 0
## 852 4 7 0 0 0
## 864 4 6 0 0 0
## 876 5 8 0 0 0
## 879 5 5 0 0 0
## 883 5 6 0 0 0
## 884 4 5 0 0 0
## 917 5 3 0 0 0
## 922 5 1 0 0 0
## 928 5 6 0 0 0
## 931 5 2 0 1 1
## 935 5 3 0 0 0
## 947 5 6 0 0 0
## 990 4 8 1 0 0
## 1010 4 7 0 0 0
## 1023 5 8 0 0 0
## 1031 4 2 0 0 0
## 1036 5 8 0 0 0
## 1068 5 2 0 0 0
## 1070 4 3 0 0 0
## 1073 5 8 0 0 0
## 1075 4 5 0 1 1
## 1090 5 8 0 0 0
## 1099 5 5 0 0 0
## 1103 5 6 0 1 1
## 1105 3 6 1 0 0
## 1106 2 8 0 0 0
## 1110 5 7 0 0 0
## 1161 4 7 0 1 0
## 1178 4 3 0 0 0
## 1184 4 4 0 0 0
## 1186 5 1 0 0 0
## 1202 5 6 0 0 0
## 1231 5 1 0 0 0
## 1232 4 8 0 0 0
## 1241 5 4 0 0 0
## 1242 4 6 0 0 0
## 1244 3 6 0 0 0
## 1272 5 7 0 0 0
## 1283 5 2 0 0 0
## 1286 4 1 0 0 1
## 1289 5 2 0 1 1
## 1291 4 9 0 0 0
## 1294 5 8 0 0 0
## 1309 5 6 0 0 0
## 1311 3 9 1 0 0
## 1316 1 17 0 0 0
## 1317 5 4 1 0 0
## 1318 2 7 0 0 0
## 1339 4 2 0 0 0
## 1370 5 1 1 0 1
## 1374 3 5 0 0 0
## 1383 4 7 0 0 0
## 1387 4 8 0 0 0
## 1398 3 9 0 0 0
## 1400 2 8 0 0 0
## 1431 5 8 1 0 0
## 1438 5 8 0 0 0
## 1449 5 2 0 0 0
## 1457 4 7 0 1 1
## 1484 5 5 0 0 0
## 1491 5 8 1 0 0
## 1499 4 5 0 0 0
## 1512 4 3 0 0 1
## 1518 3 8 0 0 0
## 1531 3 8 0 0 0
## 1537 4 2 0 0 0
## 1553 5 4 0 0 0
## 1581 4 9 1 0 0
## 1584 3 7 0 0 0
## 1668 5 5 0 0 0
## 1670 5 2 0 0 0
## 1699 5 8 0 0 0
## 1725 5 8 0 0 0
## 1735 5 6 0 0 0
## 1753 3 7 1 0 0
## 1778 3 6 1 0 0
## 1790 1 0 0 0 0
## 1804 5 4 0 0 0
## 1811 5 2 0 0 0
## 1813 2 9 0 0 0
## 1822 3 6 0 0 0
## 1825 4 2 0 1 1
## 1847 4 2 0 0 0
## 1874 5 3 0 0 0
## 1881 4 1 0 0 1
## 1895 5 5 0 1 0
## 1897 5 8 0 1 0
## 1924 5 2 1 0 1
## 1957 5 7 0 0 0
## 1959 0 1 0 0 0
## 1964 4 9 0 0 0
## 1971 3 4 0 0 0
## 1980 4 4 0 0 0
## 1991 3 8 1 0 0
## 1994 5 3 0 0 0
## 2018 3 8 1 0 0
## 2032 5 6 0 0 0
## 2047 4 2 0 0 0
## 2056 5 5 0 0 0
## 2060 4 8 0 0 0
## 2061 5 8 0 0 0
## 2075 5 6 0 0 0
## 2077 4 6 0 1 0
## 2086 4 7 0 0 0
## 2091 2 5 0 0 0
## 2111 2 8 0 0 0
## 2117 2 9 0 0 0
## 2123 4 4 0 0 1
## 2138 4 6 0 0 0
## 2146 5 1 1 0 1
## 2151 5 3 0 0 0
## 2156 3 7 1 0 0
## 2175 3 6 1 0 0
## 2176 4 6 1 0 0
## 2178 2 8 0 0 0
## 2212 4 5 0 0 0
## AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response
## 1 0 0 0 3 11 1
## 20 0 0 0 3 11 0
## 24 0 0 0 3 11 0
## 30 0 0 0 3 11 0
## 39 0 0 0 3 11 0
## 44 0 0 0 3 11 1
## 65 0 0 0 3 11 0
## 78 0 0 0 3 11 0
## 103 0 0 0 3 11 1
## 109 0 0 0 3 11 0
## 121 0 0 0 3 11 0
## 134 0 0 0 3 11 0
## 142 0 0 0 3 11 0
## 164 0 0 0 3 11 0
## 166 1 0 0 3 11 1
## 188 0 0 0 3 11 0
## 189 0 0 0 3 11 1
## 205 0 0 0 3 11 0
## 208 0 0 0 3 11 0
## 216 0 0 0 3 11 0
## 238 0 0 0 3 11 0
## 257 0 0 0 3 11 1
## 260 0 0 0 3 11 1
## 271 0 0 0 3 11 0
## 279 0 0 0 3 11 0
## 293 0 0 0 3 11 0
## 311 0 0 0 3 11 0
## 313 0 0 0 3 11 0
## 327 0 0 0 3 11 0
## 333 0 0 0 3 11 0
## 374 0 0 0 3 11 1
## 391 0 0 0 3 11 0
## 445 0 0 0 3 11 1
## 467 0 0 0 3 11 0
## 470 0 0 0 3 11 1
## 481 0 0 0 3 11 0
## 490 0 0 0 3 11 0
## 502 0 0 0 3 11 0
## 512 0 0 0 3 11 1
## 516 0 0 0 3 11 0
## 519 0 0 0 3 11 0
## 530 0 0 0 3 11 0
## 534 0 0 0 3 11 1
## 558 0 0 0 3 11 0
## 569 0 0 0 3 11 0
## 571 0 0 0 3 11 0
## 574 1 0 0 3 11 1
## 593 0 0 0 3 11 1
## 631 0 0 0 3 11 0
## 647 0 0 0 3 11 0
## 668 0 0 0 3 11 0
## 670 0 0 0 3 11 0
## 677 0 0 0 3 11 0
## 679 0 0 0 3 11 0
## 680 0 0 0 3 11 0
## 696 0 0 0 3 11 0
## 703 0 0 0 3 11 0
## 711 0 0 0 3 11 0
## 724 0 0 0 3 11 0
## 738 0 0 0 3 11 0
## 752 0 0 0 3 11 0
## 753 0 0 0 3 11 0
## 756 0 0 0 3 11 0
## 767 0 0 0 3 11 0
## 775 0 0 0 3 11 1
## 778 0 0 0 3 11 0
## 782 0 0 0 3 11 0
## 790 0 0 0 3 11 0
## 805 0 0 0 3 11 0
## 816 0 0 0 3 11 0
## 824 0 0 0 3 11 0
## 827 0 0 0 3 11 0
## 838 0 0 0 3 11 0
## 852 0 0 0 3 11 0
## 864 0 0 0 3 11 0
## 876 0 0 0 3 11 0
## 879 0 0 0 3 11 0
## 883 0 0 0 3 11 0
## 884 0 0 0 3 11 1
## 917 0 0 0 3 11 0
## 922 0 0 0 3 11 0
## 928 0 0 0 3 11 0
## 931 1 0 0 3 11 1
## 935 0 0 0 3 11 0
## 947 0 0 0 3 11 0
## 990 1 0 0 3 11 1
## 1010 0 0 0 3 11 1
## 1023 0 0 0 3 11 0
## 1031 1 0 0 3 11 0
## 1036 0 0 0 3 11 1
## 1068 0 0 0 3 11 0
## 1070 0 0 0 3 11 1
## 1073 0 0 0 3 11 0
## 1075 1 1 0 3 11 1
## 1090 0 0 0 3 11 0
## 1099 1 0 0 3 11 1
## 1103 0 0 0 3 11 0
## 1105 0 0 0 3 11 1
## 1106 0 0 0 3 11 0
## 1110 0 0 0 3 11 0
## 1161 0 0 0 3 11 0
## 1178 0 0 0 3 11 1
## 1184 0 0 0 3 11 0
## 1186 0 0 0 3 11 0
## 1202 0 0 0 3 11 0
## 1231 0 0 0 3 11 0
## 1232 0 0 0 3 11 0
## 1241 0 0 0 3 11 0
## 1242 0 0 0 3 11 0
## 1244 0 0 0 3 11 1
## 1272 0 0 0 3 11 0
## 1283 0 0 0 3 11 0
## 1286 0 0 0 3 11 1
## 1289 1 0 0 3 11 1
## 1291 0 0 0 3 11 0
## 1294 0 0 0 3 11 0
## 1309 0 0 0 3 11 0
## 1311 0 0 1 3 11 1
## 1316 0 0 0 3 11 0
## 1317 0 0 0 3 11 1
## 1318 0 0 0 3 11 0
## 1339 0 0 0 3 11 1
## 1370 0 0 0 3 11 0
## 1374 0 0 0 3 11 0
## 1383 0 0 0 3 11 0
## 1387 0 0 0 3 11 1
## 1398 0 0 0 3 11 0
## 1400 0 0 0 3 11 0
## 1431 0 0 0 3 11 0
## 1438 0 0 0 3 11 0
## 1449 0 0 0 3 11 0
## 1457 0 0 0 3 11 1
## 1484 0 0 0 3 11 0
## 1491 0 0 0 3 11 0
## 1499 0 0 0 3 11 0
## 1512 1 0 0 3 11 1
## 1518 0 0 0 3 11 0
## 1531 0 0 0 3 11 1
## 1537 1 0 0 3 11 0
## 1553 0 0 0 3 11 0
## 1581 0 0 0 3 11 0
## 1584 0 0 0 3 11 0
## 1668 0 0 0 3 11 0
## 1670 0 0 0 3 11 1
## 1699 0 0 0 3 11 0
## 1725 0 0 0 3 11 0
## 1735 0 0 0 3 11 0
## 1753 0 0 0 3 11 0
## 1778 0 0 0 3 11 0
## 1790 0 0 0 3 11 0
## 1804 1 0 0 3 11 0
## 1811 0 0 0 3 11 1
## 1813 0 0 0 3 11 1
## 1822 0 0 0 3 11 0
## 1825 1 0 0 3 11 1
## 1847 0 0 0 3 11 0
## 1874 0 0 0 3 11 0
## 1881 0 0 0 3 11 0
## 1895 0 0 0 3 11 0
## 1897 1 0 0 3 11 1
## 1924 1 0 0 3 11 1
## 1957 0 0 0 3 11 0
## 1959 0 0 0 3 11 0
## 1964 0 0 0 3 11 0
## 1971 0 0 0 3 11 0
## 1980 1 0 0 3 11 1
## 1991 0 0 0 3 11 0
## 1994 0 0 0 3 11 0
## 2018 0 0 0 3 11 0
## 2032 0 0 0 3 11 1
## 2047 0 0 0 3 11 0
## 2056 0 0 0 3 11 0
## 2060 0 0 0 3 11 0
## 2061 0 0 0 3 11 0
## 2075 0 0 0 3 11 0
## 2077 0 1 0 3 11 0
## 2086 0 0 0 3 11 0
## 2091 0 0 0 3 11 0
## 2111 0 0 0 3 11 0
## 2117 0 0 0 3 11 1
## 2123 0 0 0 3 11 1
## 2138 0 0 0 3 11 1
## 2146 1 0 0 3 11 0
## 2151 0 0 0 3 11 0
## 2156 0 0 0 3 11 0
## 2175 0 0 0 3 11 0
## 2176 0 0 0 3 11 1
## 2178 0 0 0 3 11 0
## 2212 0 0 0 3 11 0
## actual_age Total_Purch test
## 1 64 25 TECH
## 20 39 12 TECH
## 24 70 20 TECH
## 30 58 11 TECH
## 39 41 21 TECH
## 44 25 10 TECH
## 65 51 19 TECH
## 78 58 11 TECH
## 103 30 17 TECH
## 109 43 20 TECH
## 121 69 19 TECH
## 134 33 12 TECH
## 142 49 17 TECH
## 164 54 7 TECH
## 166 73 25 TECH
## 188 67 17 TECH
## 189 48 15 TECH
## 205 44 20 TECH
## 208 67 12 TECH
## 216 45 16 TECH
## 238 33 20 TECH
## 257 51 11 TECH
## 260 68 14 TECH
## 271 45 16 TECH
## 279 72 16 TECH
## 293 33 13 TECH
## 311 60 23 TECH
## 313 64 18 TECH
## 327 122 15 TECH
## 333 69 16 TECH
## 374 53 17 TECH
## 391 61 11 TECH
## 445 37 14 TECH
## 467 44 21 TECH
## 470 47 12 TECH
## 481 57 15 TECH
## 490 31 9 TECH
## 502 75 17 TECH
## 512 34 10 TECH
## 516 57 17 TECH
## 519 52 17 TECH
## 530 37 14 TECH
## 534 57 14 TECH
## 558 47 16 TECH
## 569 70 20 TECH
## 571 70 7 TECH
## 574 66 22 TECH
## 593 35 12 TECH
## 631 57 15 TECH
## 647 55 12 TECH
## 668 58 23 TECH
## 670 39 15 TECH
## 677 52 15 TECH
## 679 51 13 TECH
## 680 63 12 TECH
## 696 73 14 TECH
## 703 46 11 TECH
## 711 56 16 TECH
## 724 48 19 TECH
## 738 49 22 TECH
## 752 69 19 TECH
## 753 66 12 TECH
## 756 56 8 TECH
## 767 50 22 TECH
## 775 53 24 TECH
## 778 76 14 TECH
## 782 60 9 TECH
## 790 70 10 TECH
## 805 67 19 TECH
## 816 63 22 TECH
## 824 49 14 TECH
## 827 44 20 TECH
## 838 47 13 TECH
## 852 46 13 TECH
## 864 50 23 TECH
## 876 59 24 TECH
## 879 56 18 TECH
## 883 49 25 TECH
## 884 65 20 TECH
## 917 45 21 TECH
## 922 69 19 TECH
## 928 69 22 TECH
## 931 68 18 TECH
## 935 42 13 TECH
## 947 64 16 TECH
## 990 40 22 TECH
## 1010 68 25 TECH
## 1023 41 13 TECH
## 1031 59 14 TECH
## 1036 37 18 TECH
## 1068 56 13 TECH
## 1070 65 16 TECH
## 1073 44 26 TECH
## 1075 63 22 TECH
## 1090 35 20 TECH
## 1099 38 16 TECH
## 1103 50 14 TECH
## 1105 38 12 TECH
## 1106 55 6 TECH
## 1110 48 16 TECH
## 1161 64 17 TECH
## 1178 51 16 TECH
## 1184 56 8 TECH
## 1186 49 14 TECH
## 1202 49 15 TECH
## 1231 74 10 TECH
## 1232 43 16 TECH
## 1241 49 17 TECH
## 1242 58 23 TECH
## 1244 66 16 TECH
## 1272 70 13 TECH
## 1283 67 10 TECH
## 1286 75 15 TECH
## 1289 68 18 TECH
## 1291 51 16 TECH
## 1294 45 19 TECH
## 1309 63 15 TECH
## 1311 44 16 TECH
## 1316 39 2 TECH
## 1317 76 17 TECH
## 1318 42 8 TECH
## 1339 28 17 TECH
## 1370 73 20 TECH
## 1374 49 17 TECH
## 1383 52 14 TECH
## 1387 64 14 TECH
## 1398 31 11 TECH
## 1400 55 6 TECH
## 1431 64 26 TECH
## 1438 56 17 TECH
## 1449 58 22 TECH
## 1457 52 18 TECH
## 1484 46 19 TECH
## 1491 46 30 TECH
## 1499 41 12 TECH
## 1512 34 15 TECH
## 1518 33 8 TECH
## 1531 58 8 TECH
## 1537 59 14 TECH
## 1553 64 25 TECH
## 1581 37 15 TECH
## 1584 56 20 TECH
## 1668 69 13 TECH
## 1670 41 16 TECH
## 1699 37 18 TECH
## 1725 38 19 TECH
## 1735 47 16 TECH
## 1753 46 10 TECH
## 1778 69 14 TECH
## 1790 55 25 TECH
## 1804 59 16 TECH
## 1811 35 16 TECH
## 1813 46 10 TECH
## 1822 66 16 TECH
## 1825 68 18 TECH
## 1847 50 14 TECH
## 1874 33 20 TECH
## 1881 67 15 TECH
## 1895 51 22 TECH
## 1897 54 18 TECH
## 1924 37 22 TECH
## 1957 46 16 TECH
## 1959 52 25 TECH
## 1964 37 11 TECH
## 1971 30 12 TECH
## 1980 46 16 TECH
## 1991 57 17 TECH
## 1994 63 17 TECH
## 2018 37 12 TECH
## 2032 51 14 TECH
## 2047 70 15 TECH
## 2056 38 21 TECH
## 2060 48 9 TECH
## 2061 61 17 TECH
## 2075 65 15 TECH
## 2077 46 10 TECH
## 2086 72 13 TECH
## 2091 47 7 TECH
## 2111 41 8 TECH
## 2117 55 11 TECH
## 2123 64 19 TECH
## 2138 57 26 TECH
## 2146 37 18 TECH
## 2151 70 17 TECH
## 2156 50 15 TECH
## 2175 48 17 TECH
## 2176 67 20 TECH
## 2178 31 8 TECH
## 2212 54 18 TECH## confidence interval for CONSERVATORY
n_1 <- nrow(cat_num_cols[which (cat_num_cols$test == 'CONSERVATORY'),])
mean.conservatory <- mean (cat_num_cols$MntGoldProds[cat_num_cols$test == 'CONSERVATORY'])
std.conservarory <- sd (cat_num_cols$MntGoldProds[cat_num_cols$test == 'CONSERVATORY'])
error_1 <- qnorm(0.975)*std.conservarory/sqrt(n_1)
mean.conservatory-error_1## [1] 99.76221mean.conservatory+error_1## [1] 108.6354## confidence interval for TECH
n_2 <- nrow(cat_num_cols[which (cat_num_cols$test == 'TECH') , ])
mean.tech <- mean (cat_num_cols$MntGoldProds[cat_num_cols$test == 'TECH'])
std.tech <- sd (cat_num_cols$MntGoldProds[cat_num_cols$test == 'TECH'])
error_2 <- qnorm(0.975)*std.tech/sqrt(n_2)
mean.tech-error_2## [1] 97.34596mean.tech+error_2## [1] 114.1249Through our analysis, we were able to see that we are going to disagree with our supervisor in that there is no correlation between the amount of gold purchased within the past two years would have more in-store purchases. This is useful information to have for our marketing department because that would let us know the projection in which the customer buying habit is going towards. If we see that less customers are purchasing in-store and opting for buying online, we can spend more of our budget towards increasing the attraction of our online store.
Label: mntFishProducts
Use of linear regression to classify the important variables that help us predict the amount spent on Fish Products for married phd-candidates.
mark_train$Binary1 <- c() #build the variable to store the values of the loop
for (i in 1:nrow(mark_train)) {
if(mark_train$Education[i] == "PhD"){
mark_train$Binary1[i] <- "1"
}else{
mark_train$Binary1[i] <- "0"
} #closing the if
} #closing the for loop
mark_train$Binary2 <- c() #build the variable to store the values of the loop
for (i in 1:nrow(mark_train)) {
if(mark_train$Marital_Status[i] == "Married"){
mark_train$Binary2[i] <- "1"
}else{
mark_train$Binary2[i] <- "0"
} #closing the if
}
mark_train$Binary1<-as.numeric(mark_train$Binary1)
mark_train$Binary2<-as.numeric(mark_train$Binary2)
EDUXMAR<-function(var1,var2){
PHDXMAR<-va1*var2
return(PHDXMAR)
}
mark_train$Binary3 <- c(mark_train$Binary1*mark_train$Binary2)
my_logit <- glm(MntFishProducts~Binary1+Binary2+Binary3, data=mark_train)
summary(my_logit)
res <- resid(my_logit)
# Produce residual vs. fitted plot
plot(fitted(my_logit), res, main="Residual vs. fitted Values", xlab='Fitted', ylab='Residuals')
qqnorm(res)
mark_test$Binary1 <- c() #build the variable to store the values of the loop
for (i in 1:nrow(mark_test)) {
if(mark_test$Education[i] == "PhD"){
mark_test$Binary1[i] <- "1"
}else{
mark_test$Binary1[i] <- "0"
} #closing the if
} #closing the for loop
mark_test$Binary2 <- c() #build the variable to store the values of the loop
for (i in 1:nrow(mark_test)) {
if(mark_test$Marital_Status[i] == "Married"){
mark_test$Binary2[i] <- "1"
}else{
mark_test$Binary2[i] <- "0"
} #closing the if
}
mark_test$Binary1<-as.numeric(mark_test$Binary1)
mark_test$Binary2<-as.numeric(mark_test$Binary2)
EDUXMAR<-function(var1,var2){
PHDXMAR<-va1*var2
return(PHDXMAR)
}
mark_test$Binary3 <- c(mark_test$Binary1*mark_test$Binary2)
my_logit1 <- glm(MntFishProducts~Binary1+Binary2+Binary3, data=mark_test)
summary(my_logit1)
res <- resid(my_logit1)
# Produce residual vs. fitted plot
plot(fitted(my_logit1), res, main="Residual vs. fitted Values", xlab='Fitted', ylab='Residuals')
qqnorm(res)
- If the customer has a Phd, the amount spent on fish is reduced by 15.5$ - If the customer is married, the amount spent on fish is reduced by 4.5$ - If the customer has a Phd and is married, an additonal amount of 4.6$ is spent on fish.
Pvalue of Binary3(“Married Phd Candidates¨ vs amount spent on fish) is 0.42 which significantly high, that means”Married PhD candidates” dont have a significant relation with the amount spent on fish.
# We tried with all the factors available to see if there is any relation with the amount spent on fish.
my_logit2 <- try(lm(MntFishProducts~., data=mark_test))
summary(my_logit2)##
## Call:
## lm(formula = MntFishProducts ~ ., data = mark_test)
##
## Residuals:
## Min 1Q Median 3Q Max
## -146.883 -15.659 -1.858 9.437 159.071
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.156e+02 3.945e+02 -1.561 0.11942
## ID 7.943e-04 5.874e-04 1.352 0.17704
## Year_Birth 4.029e-01 1.796e-01 2.244 0.02537 *
## EducationBasic 1.653e+01 1.648e+01 1.003 0.31648
## EducationGraduation 6.629e+00 7.127e+00 0.930 0.35282
## EducationMaster 6.009e+00 8.106e+00 0.741 0.45894
## EducationPhD 3.650e+00 8.659e+00 0.421 0.67362
## Marital_StatusDivorced -4.467e+00 4.021e+01 -0.111 0.91160
## Marital_StatusMarried -1.114e+01 3.961e+01 -0.281 0.77861
## Marital_StatusSingle -5.589e-02 3.980e+01 -0.001 0.99888
## Marital_StatusTogether -2.924e+00 3.979e+01 -0.073 0.94145
## Marital_StatusWidow 7.089e+00 4.130e+01 0.172 0.86380
## Income -3.510e-05 5.998e-05 -0.585 0.55877
## Kidhome -8.380e+00 4.919e+00 -1.704 0.08920 .
## Teenhome 1.809e+00 4.351e+00 0.416 0.67770
## Dt_Customer -9.632e-03 1.127e-02 -0.855 0.39320
## Recency -7.872e-02 6.812e-02 -1.156 0.24857
## MntWines 1.229e-02 1.092e-02 1.125 0.26116
## MntFruits 3.233e-01 6.442e-02 5.018 7.84e-07 ***
## MntMeatProducts 4.273e-02 1.478e-02 2.890 0.00406 **
## MntSweetProducts 4.521e-01 5.824e-02 7.763 6.96e-14 ***
## MntGoldProds 6.844e-02 4.466e-02 1.532 0.12619
## NumDealsPurchases -6.474e-01 1.296e+00 -0.500 0.61765
## NumWebPurchases 1.378e+00 1.126e+00 1.224 0.22157
## NumCatalogPurchases 9.803e-01 1.193e+00 0.822 0.41168
## NumStorePurchases -1.758e+00 9.239e-01 -1.903 0.05778 .
## NumWebVisitsMonth -2.619e+00 1.252e+00 -2.092 0.03708 *
## AcceptedCmp3 -8.320e+00 8.214e+00 -1.013 0.31172
## AcceptedCmp4 -1.202e+01 7.472e+00 -1.609 0.10845
## AcceptedCmp5 -2.185e+01 9.359e+00 -2.335 0.02003 *
## AcceptedCmp1 1.703e+01 9.026e+00 1.887 0.05985 .
## AcceptedCmp2 -2.188e+01 2.134e+01 -1.025 0.30577
## Complain 2.156e+00 2.030e+01 0.106 0.91544
## Z_CostContact NA NA NA NA
## Z_Revenue NA NA NA NA
## Response -2.597e+00 6.600e+00 -0.394 0.69411
## CountryCA 3.578e+00 9.763e+00 0.366 0.71421
## CountryGER -6.599e-01 1.145e+01 -0.058 0.95407
## CountryIND 3.812e+00 1.018e+01 0.374 0.70831
## CountryME 7.982e+01 4.091e+01 1.951 0.05176 .
## CountrySA 6.642e+00 9.131e+00 0.727 0.46737
## CountrySP 4.504e+00 8.271e+00 0.545 0.58637
## CountryUS -5.776e-01 1.215e+01 -0.048 0.96210
## actual_age NA NA NA NA
## Binary1 NA NA NA NA
## Binary2 NA NA NA NA
## Binary3 1.729e+00 9.336e+00 0.185 0.85320
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38.46 on 402 degrees of freedom
## Multiple R-squared: 0.6025, Adjusted R-squared: 0.562
## F-statistic: 14.86 on 41 and 402 DF, p-value: < 2.2e-16plot(my_logit2)
my_logit3 <- glm(MntFishProducts~NumCatalogPurchases+Kidhome+Teenhome
, data=mark_test)
summary(my_logit3)##
## Call:
## glm(formula = MntFishProducts ~ NumCatalogPurchases + Kidhome +
## Teenhome, data = mark_test)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -103.767 -26.539 -6.102 8.020 192.628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.0086 5.6373 5.855 9.32e-09 ***
## NumCatalogPurchases 8.5759 0.9851 8.705 < 2e-16 ***
## Kidhome -23.9065 5.0151 -4.767 2.55e-06 ***
## Teenhome -8.7885 4.2315 -2.077 0.0384 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2261.549)
##
## Null deviance: 1495704 on 443 degrees of freedom
## Residual deviance: 995081 on 440 degrees of freedom
## AIC: 4695.4
##
## Number of Fisher Scoring iterations: 2We can see that the catalogue purchases, the Kidhome and the Teen home all have relations with the amount of money spent on fish. We can see that 7.9 is the coefficient for the catalogues purchases, that means more amount is spend on fish through the catalogues.If the customer is in the kidhome category, the amount spent on fish is reduced by 18.6. If the customer is in the teenhome category, the amount spent on fish is reduced by 16.5$
We are testing if the amount spent on wines is related to how many kids there is at home. HO = MntWines and Kidhome are independent Ha = MntWines and Kidhome are dependent
my_logit2 <- (lm(MntWines~Kidhome, data=mark_test))
summary(my_logit2)##
## Call:
## lm(formula = MntWines ~ Kidhome, data = mark_test)
##
## Residuals:
## Min 1Q Median 3Q Max
## -460.0 -134.8 -82.0 135.0 1139.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 460.00 17.16 26.81 <2e-16 ***
## Kidhome -320.20 24.37 -13.14 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.8 on 442 degrees of freedom
## Multiple R-squared: 0.2808, Adjusted R-squared: 0.2792
## F-statistic: 172.6 on 1 and 442 DF, p-value: < 2.2e-16Pvalue is significantly small so we ave the enough information to reject H0 and to accept Ha.For every additional kid a home has, the amount spent on wines is reduced by 312.5$.